dissertationes de agricultura high throughput measurement - Lirias

Katholieke Universiteit Leuven

Faculteit Bio-ingenieurswetenschappen

DISSERTATIONES DE AGRICULTURA

Doctoraatsproefschrift nr. 789 aan de Faculteit

Bio-ingenieurswetenschappen van de K.U.Leuven

HIGH THROUGHPUT MEASUREMENT OFTASTE COMPONENTS OF FRUIT JUICES

Promotoren:

Prof. J. Lammertyn, K.U.Leuven

Prof. B. Nicolaı, K.U.Leuven

Leden van de jury:

Prof. E. Decuypere, voorzitter,

K.U.Leuven

Prof. W. Keulemans, K.U.Leuven

Prof. A. Legin, Saint-Petersburg

University, Russia

Prof. R. Schoonheydt, K.U.Leuven

Prof. E. Schrevens, K.U.Leuven

Dr. Ir. B. Verlinden, VCBT vzw

.

Proefschrift voorgedragen tot

het behalen van de graad van

Doctor in de

Bio-ingenieurswetenschappen

door

Katrien BEULLENS

APRIL 2008

Voorwoord

Bladerend door de proefdrukken lijkt dit doctoraat op een reisdocument.

Figuurlijk, omdat de verschillende experimenten vertrek- en aankomstpunten

zijn geweest tijdens de afgelopen vier jaar onderzoek. Letterlijk, omdat

de metingen in Rusland, de Verenigde Staten en Belgie uitgevoerd werden

in samenwerking met kleurrijke persoonlijkheden van verschillende natio-

naliteiten. Dit alles maakte het een ontzettend boeiende en unieke reis.

Zonder enkele bijzondere mensen had ik mijn eindbestemming nooit bereikt.

Daarom een woordje van dank.

Mijn dank gaat allereerst uit naar mijn promotoren, Prof. Bart Nicolaı

en Prof. Jeroen Lammertyn. Bart, dank zij jou kreeg ik het ticket om

mijn reis aan te vatten. Het was een plezier om deel uit te maken van

jouw groep. Je hebt me steeds de mogelijkheid gegeven om verder te gaan.

Jeroen, jij hebt me enorm geholpen met het uitspitten van de reisgidsen in de

wereld van FTIR en multivariate statistiek. Bedankt voor het vertrouwen,

de bemoedigingen en de nauwgezette opvolging van mijn werk.

My gratitude also goes to Prof. Andrey Legin. Andrey, thanks for your

time and interest in my work and for giving me the chance to experience

the coldest month of my life at Saint-Petersburg University. I would also

like to thank Dima and Alisa from the Laboratory of Chemical Sensors of

Saint-Petersburg University for their cooperation during my stay at their

’electronic tongue lab’.

Verder wens ik Prof. Decuypere, voorzitter van de jury, en Prof. Keule-

mans, Prof. Legin, Prof. Schrevens, Prof. Schoonheydt en Dr. Ir. Verlin-

den, leden van de jury, te bedanken voor het kritisch bestuderen van mijn

doctoraatstekst.

iii

iv

I would like to thank Prof. Irudayaraj for giving me the opportunity to

take my first steps in the field of FTIR spectroscopy in his lab at Penn State

College. Prof. Schoonheydt wil ik speciaal bedanken voor het openstellen

van zijn laboratorium voor de FTIR experimenten die hierop volgden.

Muchas gracias a Ruby Raquel Ormeno Ponce de Leon por ajudarme

con el trabajo del infra rojo. Disfrute mucho el ano que trabajamos juntos

en tu tesina.

I also want to thank Peter Meszaros for his help with the development of

the SIA-ATR-FTIR system and for giving us the opportunity to work with

the ASTREE ET.

Bedankt Elfie en Bert, voor de vele praktische hulp die ik van jullie

kreeg. Dank zij jullie werden duizenden tomaten gemixt en gecrusht in een

oogwenk. Samenwerken met jullie was een leuk avontuur.

Dit doctoraatswerk was niet mogelijk geweest zonder de financiele steun

van het IWT-Vlaanderen en een bilaterale samenwerking tussen de Univer-

siteit van Sint-Petersburg en de K.U. Leuven. Ik wens de leden van de

gebruikerscommissie van het CLO/IWT/040726 project te bedanken voor

hun opbouwende kritiek.

Tijdens mijn reis ben ik veel leuke mensen tegengekomen. Bedankt

aan al mijn huidige en ex-collegas van MeBioS en het VCBT: Amalia,

Andrew, Anh, Annemie, Ann, Ayenew, Benny, BertV, BertVdb, Bram,

Carine, Christine, Dinh, Elfie, ElsB, ElsVan, ElsVer, Erika, Evelien, Evgeny,

Fernando, Filip, Fumihiko, Hibru, Inge, Janina, Javier, JeroenP, JeroenT,

Josee, Jurgen, Justyna, Kate, Katrijn, Kris, Maarten, Mailin, Mari Carmen,

Michele, Mulugeta, Nahor, Nhi, Nicolas, Nico, Pablo, Pal, Pathompong,

Patrick, Perla, Peter, Pieter, Romina, Sabina, Sabine, Sofie, Steven, StijnS,

StijnVer, Tongchai, Tri, Tuan, Uzuki, Victor, Violetta, Wendy and Yegermal.

Thanks for your help, the lunches at ViaVia and the pleasant times and

laughther inside and outside the lab.

Special thanks go to two very dear friends I made during my stay at the

lab: Amalia and Mari Carmen. Prequitas, muchas gracias por las risas y

las discusiones mas y (en general) menos cientıficas. Echo de menas vuestra

presencia en Belgica. Espero que podemos estar juntos en algun lugar del

mundo en algun momento de nuestra vida!

Voorwoord v

Ook een woordje van dank aan mijn vrienden die niks met MeBioS te

maken hebben. Iedereen van de scouts, de Spaanse bende, mijn assistenten-

fractiegenoten en al mijn andere vrienden, bedankt voor de vele momenten

van ontspanning en plezier die mijn reis vaak wat luchtiger maakten. Ook

voor jullie is dit het einde van een tijdperk: het is gedaan met de gratis

tomaten, appels en bananen...

Tenslotte, wil ik mijn ouders, broer Geert en schoonzus Nynke bedanken

voor hun betrokkenheid, vertrouwen en eeuwige steun tijdens de grote avon-

turen in mijn leven en de leuke momenten samen met Jelle en Wybe. Zonder

de mogelijkheden die jullie me gegeven hebben, had ik hier nooit gestaan!

Katrien,

April 2008

Nederlandse samenvatting

Traditioneel wordt gebruik gemaakt van sensorische proefpanels en instru-

mentele technieken om de smaak van voedingsproducten te bepalen. Sen-

sorische panels geven het meest realistische beeld van de smaak zoals die er-

varen wordt door de mens. Er zijn echter belangrijke nadelen verbonden aan

deze meettechniek, zoals de herhaalbaarheid, hoge kost en verzadiging van

de panelleden. Hogedrukvloeistofchromatografie (HPLC) en andere instru-

mentele technieken geven informatie over de chemische samenstelling van

een product en beschrijven zo het smaakprofiel van dit product. Deze tradi-

tionele technieken vereisen echter een uitvoerige en tijdsrovende staalvoor-

bereiding en getraind personeel om de toestellen te bedienen. In de voedings-

sector is er daarom een nood aan objectieve en eenvoudige hoge doorvoer-

meettechnieken voor smaakbepaling ter aanvulling van de sensorische panels.

In dit werk worden de mogelijkheden van twee snelle en objectieve meettech-

nieken, de elektronische tong (ET) en geattenueerde totale reflectie-Fourier

getransformeerde infrarood (ATR-FTIR) spectroscopie, voor smaakprofile-

ring van groenten en fruit geevalueerd.

In een eerste deel werd de performantie van de ET ontwikkeld aan de

Universiteit van Sint-Petersburg (ETSPU) vergeleken met de commercieel

beschikbare ASTREE ET (Alpha M.O.S., Toulouse, Frankrijk). Ondanks

de grote verschillen in het meetprotocol van beide systemen, toonden beide

ET’en de mogelijkheid om tomatenrassen te klassificeren op basis van hun

suiker-, zuur- en mineraalgehalte. De ETSPU kan ook de concentratie van

individuele componenten in een tomatenmatrix meten, terwijl de ASTREE

ET hier niet toe in staat is. De selectie van de sensoren blijkt echter cruciaal

om goede meetresultaten te verkrijgen.

vii

viii

Het potentieel van ATR-FTIR voor de klassificatie van rassen en de

kwantificatie van smaakcomponenten werd bestudeerd in verscheidene ex-

perimenten. De specifieke vibraties van chemische bindingen maakten het

mogelijk om smaakcomponenten te relateren aan de absorptiespectra. Eerst

werden de karakteristieke absorpties van IR licht door een reeks smaakbe-

palende componenten bepaald in pure en mengselvorm. Vervolgens toonde

ATR-FTIR aan een onderscheid te kunnen maken tussen vruchten op basis

van hun chemische samenstelling. Het systeem bewees nauwkeurige voor-

spellingen te kunnen maken indien de concentratieverschillen van de opgeme-

ten componenten groot zijn.

De mogelijkheden van de ETSPU en ATR-FTIR in kwaliteitscontrole

werden geevalueerd in een experiment waarin multivruchtensappen en de

individuele siropen waaruit ze bestaan werden gegroepeerd. Beide tech-

nieken slaagden erin de stalen succesvol te klassificeren en kleine hoeveelhe-

den siroop te kwantificeren in de multivruchtensappen. De ETSPU en ATR-

FTIR toonden hiermee aan geschikte technieken te zijn voor kwaliteitscon-

trole dank zij hun goede resultaten, gebruiksgemak en meetsnelheid.

In een volgende fase werd de ATR-FTIR opstelling uitgebreid met een

sequentieel injectie (SIA) systeem voor de automatisatie van de metingen. In

een eerste stap werd het doorstroomsysteem gebouwd en geoptimaliseerd op

basis van modeloplossingen. Vervolgens werd het SIA-ATR-FTIR systeem

gebruikt voor de groepering van tomatenrassen op basis van hun absorptie

spectrum en de predictie van hun belangrijkste smaakcomponenten. De

voordelen van SIA-ATR-FTIR, de snelle analyses en veelzijdigheid van de

eenvoudige instrumentatie, werden hierbij duidelijk.

Tenslotte werden de ET’en en SIA-ATR-FTIR toegepast om de smaak

van vruchten te bepalen zoals die gescoord wordt door een getraind sen-

sorisch panel. De ETSPU en ASTREE ET waren in staat om de zuurheid,

het zoutgehalte en het umami gehalte van een tomaat te voorspellen in enkele

minuten tijd. De predictie van zoetheid met de ETSPU vereist de selectie

van specifieke sensoren. SIA-ATR-FTIR kan nauwkeurige voorspellingen

maken van alle bestudeerde smaken. Zowel de ET als ATR-FTIR toonden

aan potentieel te hebben in vele toepassingen in de voedingsindustrie.

Abstract

Sensory and instrumental techniques are traditionally used to determine the

taste of food products. Sensory panels give by far the most realistic image

of the taste of a product as perceived by humans, however, they have se-

rious drawbacks such as reproducibility, high cost and taste saturation of

the panelist. High pressure liquid chromatography (HPLC) and other in-

strumental techniques give information on the chemical composition of the

sample and, hence, are useful to describe the taste profile of the product.

These traditional techniques often require a laborious and time-consuming

sample preparation and skilled people to operate the equipment. In food

research there is a need for objective high throughput taste profiling to

complement sensory panels. In this thesis, the potential of two rapid and

objective measurement techniques, the electronic tongue (ET) and attenu-

ated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR),

for taste profiling of fruit is evaluated.

The possibilities of the ET developed at the University of Saint-Petersburg

(ETSPU) were compared to the commercially available ASTREE ET devel-

oped by Alpha M.O.S. (Toulouse, France). Considerable differences in the

measurement protocol of both systems were listed. Despite the differences,

both ET’s did show the possibility to classify tomato cultivars based on

their sugar, acid and mineral content. The ETSPU can predict individual

compounds in a tomato matrix, while the ASTREE ET cannot quantify

the concentration of any of the studied compounds. A proper selection of

sensors, however, is crucial to reach reliable and repeatable results with the

ETSPU.

ATR-FTIR allows relating taste compounds directly to the absorbance

ix

x

spectra through the study of specific vibrations of chemical bonds. The po-

tential to use this technique for the classification of cultivars and the quan-

tification of taste compounds was studied in several experiments. First, pure

compounds and mixtures were analyzed to determine the important absorp-

tion bands of IR light which are characteristic for each taste compound.

Second, real fruit samples were analyzed. ATR-FTIR showed to be able

to distinguish between fruit samples based on their chemical composition.

The system also proved to be very accurate in the quantification of taste

compounds when the concentration ranges are large and the influence of the

matrix is low.

Next, the potential of the ETSPU and ATR-FTIR as tools for rapid qual-

ity control was studied. Using these systems it is possible to group multifruit

juices and the individual syrups they are made of. Both rapid techniques

made it possible to predict low concentrations of syrup in the complex multi-

fruit juice. The ETSPU and ATR-FTIR showed to be promising techniques

for quality control because of their good performance, easy use and detection

speed.

In a next phase, the ATR-FTIR system was extended with a sequen-

tial injection analysis (SIA) set-up. In a first step, the development and

optimization of the SIA-ATR-FTIR were studied into detail using model so-

lutions. Subsequently, the system was used to discriminate between tomato

cultivars based on their specific absorption of sugars and acids and to de-

termine the concentrations of the most important taste compounds. The

advantages of SIA-ATR-FTIR include rapid analysis, versatility and sim-

plicity of the flow injection instrumentation.

Finally, the potential of the ET and SIA-ATR-FTIR to determine the

taste of fruit as measured by a trained sensory panel was examined. Using

the ETSPU and the ASTREE ET sourness, saltiness and umami could be

predicted accurately. The prediction of sweetness required the selection of

specific sensors for the ETSPU. SIA-ATR-FTIR was able to make accurate

predictions on sweetness, sourness, saltiness and umami. Both the ET’s

and ATR-FTIR showed to have a potential for many applications in food

industry.

Contents

Voorwoord iii

Nederlandse samenvatting vii

Abstract ix

Contents xi

Symbols and Abbreviations xv

1 General introduction 1

1.1 Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Taste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Measurement of taste . . . . . . . . . . . . . . . . . . . . . . 5

1.4 Objectives and outline of the thesis . . . . . . . . . . . . . . . 6

2 Literature review 9

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 Traditional techniques for the measurement of taste . . . . . 10

2.2.1 High performance liquid chromatography . . . . . . . 10

2.2.2 Enzymatic analysis . . . . . . . . . . . . . . . . . . . . 11

2.2.3 Atomic absorption spectroscopy . . . . . . . . . . . . . 12

2.2.4 Soluble solids content and titratable acidity . . . . . . 12

2.2.5 Sensory analysis . . . . . . . . . . . . . . . . . . . . . 12

2.3 Electronic tongue technology . . . . . . . . . . . . . . . . . . 13

2.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 13

2.3.2 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 14

xi

xii CONTENTS

2.3.3 Instrumentation . . . . . . . . . . . . . . . . . . . . . 19

2.3.4 Applications . . . . . . . . . . . . . . . . . . . . . . . 22

2.4 Fourier transform infrared spectroscopy . . . . . . . . . . . . 24

2.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 24

2.4.2 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.4.3 Instrumentation . . . . . . . . . . . . . . . . . . . . . 29

2.4.4 Applications . . . . . . . . . . . . . . . . . . . . . . . 36

2.5 Multivariate analysis techniques . . . . . . . . . . . . . . . . . 37

2.5.1 Data pretreatment . . . . . . . . . . . . . . . . . . . . 38

2.5.2 Principal component analysis . . . . . . . . . . . . . . 39

2.5.3 Principal component regression . . . . . . . . . . . . . 40

2.5.4 Partial least squares . . . . . . . . . . . . . . . . . . . 40

3 Electronic tongue technology 45

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . 47

3.2.1 Samples . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.2.2 Electronic tongues . . . . . . . . . . . . . . . . . . . . 50

3.2.3 Reference techniques . . . . . . . . . . . . . . . . . . . 54

3.2.4 Statistical analysis . . . . . . . . . . . . . . . . . . . . 56

3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

3.3.1 Classification of apple and tomato cultivars and quan-

tification of taste compounds . . . . . . . . . . . . . . 58

3.3.2 Comparison of two electronic tongues . . . . . . . . . 70

3.3.3 Quality control of fruit juices . . . . . . . . . . . . . . 79

3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85



3.4.2 Comparison between two electronic tongues . . . . . . 87


3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

4 Fourier transform infrared spectroscopy 95

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

CONTENTS xiii

4.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . 98

4.2.1 Samples . . . . . . . . . . . . . . . . . . . . . . . . . . 98

4.2.2 ATR-FTIR . . . . . . . . . . . . . . . . . . . . . . . . 102

4.2.3 Reference techniques . . . . . . . . . . . . . . . . . . . 105


4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

4.3.1 Taste compounds . . . . . . . . . . . . . . . . . . . . . 107



4.3.3 Extracted samples versus juices . . . . . . . . . . . . . 124

4.3.4 Dilutions and standard additions . . . . . . . . . . . . 133


4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

4.4.1 Classification of samples . . . . . . . . . . . . . . . . . 142

4.4.2 Quantification of taste compounds . . . . . . . . . . . 144


4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

5 Sequential injection ATR-FTIR 151

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

5.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . 153

5.2.1 Samples . . . . . . . . . . . . . . . . . . . . . . . . . . 153

5.2.2 Measurement techniques . . . . . . . . . . . . . . . . . 155

5.2.3 Optimization design . . . . . . . . . . . . . . . . . . . 156


5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

5.3.1 Optimization . . . . . . . . . . . . . . . . . . . . . . . 159

5.3.2 Data exploration of tomato samples . . . . . . . . . . 162

5.3.3 Classification of tomato cultivars . . . . . . . . . . . . 164


5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168

5.4.1 Optimization . . . . . . . . . . . . . . . . . . . . . . . 168

5.4.2 Classification of tomato cultivars . . . . . . . . . . . . 169


xiv Table of Contents

5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171

6 Relation between sensory analysis and instrumental mea-

surements 173

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173

6.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . 175

6.2.1 Samples . . . . . . . . . . . . . . . . . . . . . . . . . . 175

6.2.2 Sensory panel analysis . . . . . . . . . . . . . . . . . . 177

6.2.3 Instrumental techniques . . . . . . . . . . . . . . . . . 178


6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180

6.3.1 Addition of chemical taste compounds to a tomato juice180

6.3.2 Tomatoes with a wide range of tastes . . . . . . . . . 182

6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

6.4.1 Addition of chemical compounds to a tomato juice . . 190

6.4.2 Tomatoes with a wide range of tastes . . . . . . . . . 191

6.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

7 General conclusions and future work 195

7.1 General conclusions . . . . . . . . . . . . . . . . . . . . . . . . 195

7.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

Bibliography 201

List of publications 219

Symbols and Abbreviations

A absorbance units

ai activity of a primary ion

ao activity of oxidized species

ar activity of reduced species

AAS atomic absorption spectroscopy

ACLS augmented classical least squares

AMTIR amorphous material transmitting infrared light

ATR attenuated total reflectance

BB Box-Behnken design

BI bead injection

B(σ) imperfection of optical design

c chromophore concentration

ci predicted concentration

ci known concentration

CCD central composite design

CLS classical least squares

CV coefficient of variation

δ pathlenght difference

DA diode array

xv

xvi

ε molar extinction coefficient

Eo standard electrode potential

EHT enzymatic high throughput

EMSC extended multiple signal correction

ET electronic tongue

ETSPU electronic tongue developed at Saint-Petersburg University

F Faraday constant

FFT fast Fourier transform

FIA flow injection analysis

FTIR Fourier transform infrared spectroscopy

GC gas chromatography

GLM general linear model

HATR horizontal attenuated total reflectance

HPLC high performance liquid chromatography

I detected light intensity

I0 incident light intensity

IDS current between drain and source

IR infrared

ISFET ion-selective field effect transistors

Kij selective coefficient of the ion-selective membrane to the pri-

mary ion i in presence of an interfering ion j

l pathlength

λ wavelength of light

Symbols and Abbreviations xvii

LAPV large amplitude pulse voltametry

LC liquid chromatography

LOD limit of detection

LOV lab-on-valve

LV latent variable

mid-IR mid-infrared

MLR multiple linear regression

M.O.S. Multi Organoleptic Systems

MS mass spectrometry

MSC multiple scatter correction

n number of samples

N number of atoms in a molecule

n1 refractive index of the prism material

n2 refractive index of the medium

NIR near infrared

PC principal component

PCA principal component analysis

PCR principal component regression

P (δ) energy at detector

P (σ) incident energy at interferometer

PLS partial least squares

PLS-DA partial least squares-discriminant analysis

PVC polyvinylchloride

R correlation coefficient

R gas constant (8.314 Jmol−1K−1)

RI refractive index

xviii

RMSEC root mean square error of calibration

RMSECV root mean square error of cross validation

RPD ratio of prediction to deviation

σ wavenumber of light

SAPV small amplitude pulse voltametry

SAS Statistical Analysis System

SIA sequential injection analysis

SNV standard normal variate correction

SSC soluble solids content

S-SENCE Swedish Sensor Centre

St.Dev. standard deviation

T temperature

θc critical angle

θi angle of incident light

TA titratable acidity

UV ultraviolet

VCBT Flanders Centre of Postharvest Technology

zi electrical charge of a primary ion

zj electrical charge of an interfering ion

Chapter 1

General introduction

1.1 Quality

Fruit and vegetables are important consituents of the human diet. They

are sources of many essential vitamins and minerals, they are low in fat

and high in dietary fiber and complex carbohydrates. By a high intake of

fruit and vegetables human health will benefit and the immune system will

boost. Experts suggest that consumers should have five portions of fruit and

vegetables every day to improve general well-being. Production of fruit and

vegetables in Flanders was more than 2 million ton in 2005 (VBT, 2006).

The most important fruit in Belgium is apple, which acounts for 56% of the

total fruit production. Both the health benefit and the high production in

Belgium are motives for research concering fruit and vegetable quality.

The quality of fruit is generally determined by intrinsic properties of the

product and the appreciation of the consumer. Important attributes are

the nutritional quality determined by the energetic value, proteins, vitamins

and minerals; the convenience quality which is related to the storability and

the ease-to-handle the product; and the safety, which includes the absence

of fungi, mycotoxins and pesticides. The most important quality aspects are

safety, appearance, texture, aroma and taste. Both aroma and taste, which

are organoleptic quality attributes, are probably the most time consuming,

difficult and expensive properties to evaluate (Jongen, 2002). The focus of

the work presented in this thesis will be on the taste of fruit. The influence

1

2 1.2 Taste

of aroma and texture on the overall appreciation of a fruit will not be taken

into acount.

1.2 Taste

Taste is a very subjective quality characteristic of food products. It is a

fundamental chemical sense, like olfaction, which involves the detection of

stimuli dissolved in water, oil or saliva by the taste buds (Drewnowksi, 2001).

Humans can only taste differences in the concentration of substances, and

not absolute concentrations, and their sensitivity to levels that are lower

than those which appear in saliva is low. In addition to the concentration of

a taste stimulus, other conditions in the mouth that affect taste perception

are the temperature, viscosity, rate, duration and area of application of the

stimulus, the chemical state of the saliva and the presence of other tastants

in the solution being tasted (Meilgaard et al., 2007).

Taste is described by five gustatory perceptions, sweetness, sourness,

saltiness, umami and bitterness, caused by soluble substances in the mouth

(Meilgaard et al., 2007). The five tastes are mainly caused by the presence of

respectively sugars, organic acids, salts, monosodium-glutamate, phenolics

and alkaloids. The sensation of a taste can, however, not simply be explained

by the presence of a compound. Synergistic effects exist between different

compounds, so that the sensation of a taste cannot solely be explained by

the content of one group of compounds (Stevens et al., 1977; Salles et al.,

2003). As an example, the average composition of a tomato juice is shown

in Table 1.1.

Sweetness is mainly produced by the presence of sugars in a food product.

It is often connected to aldehydes and ketones, which contain a carbonyl

group. The average human detection threshold for sucrose in water is 10

mM. Fructose is 50% more sweet than sucrose, while glucose is 50% less sweet

(Breslin et al., 1994). Often, synergistic effects occur between sugars and

acids (Stevens et al., 1977; Fernandez-Ruiz et al., 2004). Also, the presence

of salts and some volatile compounds intensifies sweetness (De Bruyn et al.,

1971; Stevens et al., 1977; Noble, 1996; Stevenson et al., 1999).

Sourness is the taste that detects acidity and is, thus, triggered by H+

General introduction 3

ions and free acids. Malic acid is found to be up to 14% more sour than

citric acid. The average human detection threshold for HCl in water is 0.09

mM. The effect of sugars on the perception of sourness is much less intense

than the effect of acids on sweetness (Stevens et al., 1977; Baldwin et al.,

1998). The relation between pH, titratable acidity and total acids is not

clear since several authors give different statements on this subject (Petro-

Truza, 1987; Malundo et al., 1995; Fernandez-Ruiz et al., 2004). Several

volatile substances are found to have a correlation with sourness (Noble,

1996; Stevenson et al., 1999).

Table 1.1: Average composition of the dry matter content of tomato juice.

Constituent %

Fructose 25

Glucose 22

Sucrose 1

Citric acid 9

Malic acid 4

Protein 8

Dicarboxylic amino acids 2

Pectic substances 7

Cellulose 6

Hemicellulose 4

Minerals 8

Lipids 2

Ascorbic acid 0.5

Pigments 0.4

Other amino acids, vitamins 1

and polyphenols

Volatiles 0.1

Saltiness is a taste produced primarily by the presence of Na+ ions. NaCl

is the most known of all salts. It stimulates sweetness at low concentrations

and tastes somewhat sour at mid range concentrations. Other salts can

taste significantly sour or bitter in addition to salty. The average human

detection threshold for NaCl in water is 10 mM. After the taste buds have

4 1.2 Taste

adapted to NaCl, the sourness and bitterness of many salts increase (Smith

and van der Klaauw, 1995).

Bitter taste is often perceived to be unpleasant, sharp or disagreeable.

Since numerous harmful compounds, including secondary plant metabolites,

synthetic chemicals, inorganic ions and rancid fats taste bitter, bitterness

may be considered as a defense mechanism against the ingestion of potential

poisons (Meyerhof et al., 2005). The average human detection threshold for

quinine in water is 0.0008 mM.

Umami is the taste of certain amino acids, like glutamate, aspartate and

related compounds. It was first defined by Ikeda at the Imperial Univer-

sity of Tokyo in 1909. Monosodium glutamate, added to many foods to

enhance their taste, may stimulate the umami receptors. The average hu-

man detection threshold for glutamate in water is 0.07 mM. Umami taste

has characteristic qualities that differentiate it from other tastes, including a

taste-enhancing synergism between L-glutamate and 5’-ribonucleotides and

a prolonged after taste (Teranishi et al., 1999; Ninomiya, 2002).

Taste and the content of taste compounds in a fruit can be influenced

by several factors. Genetics, maturity, pre- and postharvest handling cause

significant changes in taste. Genetics is the predominant factor influencing

taste. The chemical content of fruits from different cultivars can differ sub-

stantially. Large differences in the amount of acids and sugars and their

ratio influence taste significantly (Petro-Truza, 1987; Baldwin et al., 1991).

By genetically manipulating the sugar and acid content of a fruit, the taste

will change significantly (Jones and Scott, 1983). Cultivar plays only a small

role in the total mineral content of a fruit (Davies and Winsor, 1967). Large

differences are present between ripe and unripe fruit. The changes which

occur during ripening mainly involve an increase of the sugar content and

a change of the total sugar and acid composition of the fruit (Davies and

Kempton, 1975; Petro-Truza, 1987). The condition under which the fruits

ripen influence the final composition of the fruits (Auerswald et al., 1999).

Of the evironmental factors, light has the most pronounced positive effect

on the sugar content of a fruit. As a consequence, greenhouse fruit grown

during winter contains substantially less sugar. During processing of fruit,

the sugar content can decrease depending on the time and extent of heat


treatment. The loss can be explained by caramelization, browning reactions

between sugars and amino acids, and by the formation of 5-hydroxymethyl

furfural in an acid medium upon the loss of H2O2. The acid content of

a fruit is highly dependent of the soil conditions. The potassium content

of the soil affects the total acid content of a fruit (Petersen et al., 1998).

Davies and Winsor (1967) found a positive logaritmic correlation between

the potassium level in the soil and the total acid content of a fruit. The

glutamate and aspartate content in fruit is highly dependent on the nitro-

gen and phosphate conentent in the soil. A high nitrogen content and a low

phosphate content substantially increase the concentration of these amino

acids (Davies, 1964). Fruit grown in a field contains significantly more glu-

tamate than fruits grown in a greenhouse (Yamanaka et al., 1971).

1.3 Measurement of taste

Many fruit and vegetable are determined by their typical sweet and sour

taste. Various techniques are used to determine the sugar and acid content

of plant material. Sugars and acids can be determined by techniques such

as high performance liquid chromatography (HPLC) or, after derivatiza-

tion, gas chromatography(-mass spectrometry) (GC-MS). Both techniques

require expensive apparatus and demand a considerable amount of time per

measurement (Molnar-Perl, 1999). Another method of determining specific

sugars and acids is by enzymatic analysis. Assays are available for vari-

ous sugars, citric acid, malic acid and glutamic acid. For these enzymatic

assays, sample preparation is simple and the only apparatus required is a

spectrophotometer (Vermeir et al., 2007).

Next to these more traditional techniques to determine individual chem-

ical compounds, some recent developments are available which do not de-

termine individual compounds in a direct way. In the last decade, arrays

of sensors that analyze liquids, which are referred to as electronic tongues

(ET’s), were developed. Although the development of ET’s is still in its

early stages, several applications have already been described (Vlasov et al.,

2002; Toko, 1996; Winquist et al., 1997). ET’s have proved to be successful

in discrimination and classification of food products, food quality evalua-

6 1.4 Objectives and outline of the thesis

tion, and control and process monitoring. The main advantages of ET’s are

the low cost, easy-to-handle measurement set-up and speed of the measure-

ments (Vlasov et al., 2002; Deisingh et al., 2004). Despite the positive results

found for each ET in literature, no extensive study has been published on

the use of ET technology in high throughput experiments on horticultural

produce. Also, it is not known whether some types of ET’s perform better

than others.

Another alternative for traditional instrumental techniques is Fourier

transform infrared spectroscopy (FTIR), which is a well-established tech-

nique in chemical analysis. FTIR spectrometers using an interferometer

provide high energy to the sample, scan fast and have the capability to

co-add data so that within a short time spectra can be produced from

poorly transmitting samples with acceptable signal-to-noise ratios (Wilson

and Goodfellow, 1994). Mid-IR has significant advantages over NIR for

spectral assignment, resolution and ease of quantification. Another advan-

tage is that mid-IR spectra can provide information about the physical and

chemical states of individual compounds. If combined with attenuated to-

tal reflectance (ATR) and flow injection systems, this technique offers great

advantages for food analysis (Griffiths and de Haseth, 2007). As literature

shows, a lot of research has been performed to study the potential of ATR-

FTIR for the classification of samples and the determination of different

compounds. Until now, however, no report has been published regarding

the use of ATR-FTIR to measure taste of fruits. Also, there is scope for

improving sample presentation protocols.

1.4 Objectives and outline of the thesis

The main objective of this thesis is to study the potential of rapid and

objective measurement techniques for taste profiling of fruit and vegetable.

In order to achieve this goal, different subobjectives were defined.

� to evaluate the ET and (SIA-)ATR-FTIR as rapid instrumental tech-

niques for the classification of fruit samples based on their chemical

composition


� to study the ability of the ET and (SIA-)ATR-FTIR to quantify in-

dividual taste compounds in fruit as measured using a reference tech-

nique

� to compare the potential of the ET and SIA-ATR-FTIR to determine

the taste of fruit as measured by a trained sensory panel

� to investigate the possibilities of the ET system and ATR-FTIR spec-

troscopy as tools for quality control of fruit juices.

The outline of this thesis is as follows. In Chapter 2, the state-of-the-art

of traditional and emerging instrumental techniques available for the analysis

of taste compounds and taste will be discussed. HPLC, enzymatic analysis,

atomic absorption spectroscopy (AAS), soluble solids content (SSC), titrat-

able acidity (TA), ET technology and (SIA-)ATR-FTIR will be introduced

briefly. Special attention will also go to taste analysis with trained panels,

as well as a selection of multivariate techniques to analyze the instrumental

and sensory datasets.

The potential of ET technology is discussed in Chapter 3. For the first

time, the ability of the ET developed at the University of Saint-Petersburg

(ETSPU) to classify and quantify taste compounds in apple and tomato

cultivars will be studied. The potential of this multisensor system to ana-

lyze tomato cultivars with very different taste profiles will be compared to

the commercially available ASTREE Liquid and Taste Analyzer from the

Alpha M.O.S. company. This will be the first comparison between ET’s

ever reported. Finally, the possibilities of the ETSPU as a tool for rapid

quality control will be studied. The results presented in this chapter are

published by Beullens et al. (2004, 2005a,b, 2006a, 2007a,b, 2008b), Legin

et al. (2005a) and Rudnitskaya et al. (2006).

Chapter 4 deals with the assessment of the potential of ATR-FTIR for

rapid analysis of taste compounds in apple and tomato fruit. Never before,

a structured study on the determination of taste compounds in fruit has

been reported. First, the ability of ATR-FTIR to measure taste compounds

will be studied using dilution series of three sugars and two acids. Second,

8 1.4 Objectives and outline of the thesis

the potential of ATR-FTIR to classify cultivars will be discussed. Third,

the possibility of ATR-FTIR to quantify individual chemical compounds

based on the absorbance spectrum of a sample will be studied. Finally, the

potential of ATR-FTIR as a tool for quality control of fruit juices will be

evaluated. The results of the experiments were published by Beullens et al.

(2004, 2005a,b, 2006a) and Rudnitskaya et al. (2006).

In Chapter 5 a novel sequential injection analysis (SIA) system will be

described to complement ATR-FTIR. The chapter will be divided into two

parts. The first part will deal with the development and optimization of

the SIA-ATR-FTIR technique using model solutions. The second part will

evaluate the system in an experiment using tomato samples with very dif-

ferent taste profiles. The results described in this chapter are described in

Beullens et al. (2006b, 2007c, 2008c).

The final challenge of this thesis will be the prediction of taste attributes

as measured by a trained panel using the ASTREE ET, ETSPU and SIA-

ATR-FTIR. The ability of all three techniques to predict sweetness, sour-

ness, saltiness and umami taste will be studied in Chapter 6. This will be

the first evaluation of SIA-ATR-FTIR as a technique to predict taste at-

tributes ever. The results of this work have been described in Beullens et al.

(2008b,c).

Finally, some general conclusions and suggestions for future work will be

formulated in Chapter 7.

Chapter 2

Literature review

2.1 Introduction

For the consumer, important quality attributes are flavor and food safety.

Flavor is the sensory impression of a food product which is determined

by both taste and aroma. The evaluation of flavor is generally very time

consuming, difficult and expensive. In this work, the focus will be on the

detection of taste in fruit.

Taste is a very subjective quality characteristic of food products. Five

gustatory perceptions, sweetness, sourness, saltiness, umami and bitterness,

determine the taste of a product (Meilgaard et al., 2007). The five taste

attributes are mainly caused by the presence of respectively sugars, organic

acids, salts, monosodium-glutamate and phenolics and alkaloids. Because of

some important synergistic effects, it is not possible to explain the sensation

of taste by the presence of individual chemical compounds (Stevens et al.,

1977; Salles et al., 2003).

Many fruit and vegetable are characterized by their typical sweet and

sour taste. Several techniques are used to determine the sugar and acid

content of plant material. Sugars and acids are often determined by high

pressure liquid chromatography (HPLC). This technique, however, requires

9

10 2.2 Traditional techniques for the measurement of taste

expensive apparatus and is very time consuming (Molnar-Perl, 1999). A

second method of determining specific sugars and acids is by enzymatic

analysis. For these enzymatic assays, sample preparation is easy and only a

spectrophotometer is required (Vermeir et al., 2007).

Next to the traditional techniques, some recent developments are avail-

able which do not determine individual compounds in a direct way. Sev-

eral applications of arrays of sensors, which are refered to as electronic

tongues (ET’s), in food technology have been published over the last two

decades (Vlasov et al., 2002; Toko, 1996; Winquist et al., 1997). Also Fourier

transform infrared spectroscopy (FTIR) combined with attenuated total re-

flectance (ATR) offers great advantages for the analysis of food products

(Griffiths and de Haseth, 2007). The objective of this chapter is to give an

overview of all instrumental, sensory and statistical techniques used in the

experimental part of this work and some available alternatives. In a first

part, traditional instrumental and sensory techniques available for the anal-

ysis of taste compounds and taste will be discussed (Section 2.2). Next, a

detailed study will be made on ET technology (Section 2.3) and ATR-FTIR

(Section 2.4). Finally, some techniques for multivariate statistical analysis

will be discussed (Section 2.5).

2.2 Traditional techniques for the measurement of

taste

2.2.1 High performance liquid chromatography

Since the early 1970s HPLC has evolved toward a high degree of technical

sophistication. Liquid chromatography (LC) has become a routine method

in almost all areas of instrumental analysis. LC has many applications

including separation, identification, purification and quantification of various

compounds. Chromatographic processes in general are defined as techniques

involving mass-transfer between stationary and mobile phases (Kazakevich

and Lobrutto, 2007). The LC instrument consists of a solvent reservoir,

Literature review 11

a pump, an injection device, a column, a detector and a data acquisition

system (Nollet, 1992).

The mobile phase in HPLC refers to the solvent which is continuously

pumped over the column or stationary phase. The mobile phase acts as

a carrier for the sample solution. The sample often undergoes a laborious

sample preparation like solvent extraction or purification before injection

into the loop via the injection valve (Salles et al., 2003; Kazakevich and

Lobrutto, 2007). As a sample solution flows through the column with the

mobile phase, the components bind to the column due to non-covalent inter-

actions with the column. The chemical interactions of the mobile phase and

sample with the column determine the degree of migration and separation

of components contained in the sample. Columns containing various types

of stationary phases are commercially available. After separation over the

column, the sample flows towards the detector. The detector is the compo-

nent that emits a response and signals a peak on the chromatogram. Some

of the more common detectors include the refractive index (RI) detector

for sugar analysis, ultraviolet (UV) and photodiode array (DA) detector for

acid analysis (Willard et al., 1988; Nollet, 1992).

2.2.2 Enzymatic analysis

Another method of determining specific compounds is by enzymatic analysis.

Assays are commercially available for various sugars and acids. For these en-

zymatic assays, sample preparation is simple and only a spectrophotometer

is required (Velterop and Vos, 2001). Enzymatic analysis works through the

use of specific enzymes which catalyze reactions with defined compounds.

This technique offers higher levels of sensitivity than chromatography. De-

spite all the positive aspects, commercial assays can be very expensive. The

cost per sample can be reduced by applying changes to the protocol to

enable determination of compounds in microtitre plates. Successful modi-

fication reduces the amount of sample, chemicals and enzymes needed and

increases the number of samples which can be analyzed per day (Campbell

et al., 1999; Vermeir et al., 2007).

12 2.2 Traditional techniques for the measurement of taste

2.2.3 Atomic absorption spectroscopy

Atomic absorption spectroscopy (AAS) is a technique for the determination

of the concentration of metal elements in a sample. AAS can be used to

analyze the concentration of over 62 different metals in a solution. The

technique typically uses a flame to atomize the sample. Three steps are

involved in turning a liquid sample into an atomic gas: desolvation, vapor-

ization and volatilization. A beam of light, which is produced by a hollow

cathode lamp, passes through the flame and hits a detector. A cylindrical

metal cathode containing the metal for excitation and an anode are present

in the lamp. The technique uses the wavelengths of light specifically ab-

sorbed by an element. They correspond to the energies needed to promote

electrons from one energy level to another, higher, energy level. It is possi-

ble to calculate how many energy transitions took place and, thus, find the

concentration of the element being measured (Haswell, 1991).

2.2.4 Soluble solids content and titratable acidity

The soluble solids content and titratable acidity of a sample are considered as

rapid and easily applicable techniques for the determination of respectively

the sugar and acid content. The amount of soluble solids is determined

by the refraction of visible light using a refractometer. The soluble solids

content of a fruit contains mainly sugars, however, also acids and other

compounds contribute to the refractive index. The amount of titratable

acidity is correlated with the total acidity of a sample. The method is

based on a titration with NaOH (0,1 ml/l). Despite the simplicity of these

techniques, they are limited by their non-specificity.

2.2.5 Sensory analysis

A traditional method for flavor analysis is the sensory evaluation. This

technique is used to measure those characteristics of foods and materials in

the way that they are perceived by the human senses. In the past decade,


scientists have developed sensory testing as a formalized, structured and

codified methodology so that it serves economic interests. The principal

uses of sensory techniques are in quality control, product development and

research. Applications are not only found in characterization of foodstuff

and beverages, but also in other fields such as environmental odors, diag-

nosis of illnesses, testing of pure chemicals etc. The primary function of

sensory analysis is to conduct valid and reliable tests that provide data on

which decisions can be made (Meilgaard et al., 2007). Sensory methods can

be separated into two groups: discriminant methods and descriptive meth-

ods. The purpose of a discrimination test is simply to differentiate between

samples. Overall difference tests include triangle and duo-trio tests, which

are designed to show whether panelists can detect differences between all

samples (Piggott et al., 1998). Attribute difference tests deal with one or

a few attributes and how they differ between samples. All other attributes

are ignored. Examples of these tests are paired and multiple comparison

tests. Description tests, on the other hand, aim to identify and measure

the composition of products or to determine the presence or intensity of a

characteristic. The tests involve the detection and description of both qual-

itative and quantitative sensory aspects of a product by trained panelists.

Many descriptive analysis methods have been developed. The most popu-

lar are the flavor profile method, the texture profile method, the quantita-

tive descriptive analysis method, the spectrum descriptive analysis method,

time-intensity descriptive analysis and free-choice profiling (Hootman, 1992;

Meilgaard et al., 2007).

2.3 Electronic tongue technology

2.3.1 Introduction

Intensive research and development of sensors and sensor systems has been

carried out with respect to the analysis of food. In the 1980s, the first

multisensor system designed for aroma analysis, the ’electronic nose’, was

introduced (Gardner and Bartlett, 1994). The electronic nose design is based

14 2.3 Electronic tongue technology

on similarities with the biological olfactory system, comprising an array of

nonspecific receptors (sensors) and a neural network (natural or artificial)

for data processing. In analogy to the electronic nose technology, sensor

arrays for analysis in liquids were developed in the 1990s. These ’electronic

tongues’ (ET’s) consisted of ion-selective electrodes. This resulted in the

development of a ’taste sensor’, which was applied to the recognition of foods

and the establishment of a correlation between the sensors and the five basic

tastes (Toko, 1996). Different multisensor systems have been developed

over the last decade (Vlasov et al., 2002; Ciosek and Wroblewski, 2007).

The main advantages of ET technology are the low cost, easy-to-handle

measurement set-up and speed of the measurements (Lvova et al., 2003). In

the following part, the theoretical background of the different systems used

in the experimental part of this thesis will be discussed together with other

technologies and ET’s which have been proved to be successful.

2.3.2 Theory

There are two electrochemical measurement principles that are commonly

used in ET technology: potentiometry and voltammetry. Both require at

least two electrodes and an electrolyte solution. One electrode responds to

the target molecule and is called the working electrode, the second one is of

constant potential and is called the reference electrode (Pearce et al., 2003).

Potentiometry is a zero-current-based technique, in which a potential

across a membrane on the working electrode is measured. Different types

of membrane materials have been developed, having different recognition

properties. These types of devices are widely used for the measurement

of a large number of ionic species, the most important being the pH elec-

trode, other examples are electrodes for calcium, potassium, sodium, and

chloride. The equipment for potentiometric studies includes an ion-selective

electrode, a reference electrode and a potentiometer, as shown in Figure 2.1.

A commonly used reference electrode is the Ag/AgCl electrode based on the

half-cell reaction:


V Reference electrode

Ion selective electrode

Ag/AgClAg/AgCl

Ion selective membrane

Voltage junction

Figure 2.1: Schematic set-up of potentiometric sensors (after Pearce et al. (2003)).

AgCl + e− −→ Ag + Cl− Eo = +220mV (2.1)

The electrode consists of an Ag wire coated with AgCl placed into a

solution of Cl− ions. A porous plug will serve as a voltage bridge to the

outer solution. The ion-selective electrode has a similar configuration, but

instead of a voltage bridge, an ion-selective membrane is applied. This

membrane should be non-porous, water insoluble and mechanically stable.

It should have an affinity for the selected ion that is high and selective.

Potentiometry generally assumes linear dependence between an electronic

reading and the logarithm of the activity of the primary ion in a solution.

The potential, E, follows the Nernst relation:

E = Eo − RTzF· ln ar

ao(2.2)

where Eo (V) is a constant for the system given by the standard electrode

potentials, R is the ideal gas constant (8.31 JK−1mol−1) , T is the tem-

perature (K), z is the the number of electrons transferred, F is the Faraday


constant (9.65 · 104Cmol−1) and ar and ao are the activities of the reduced

and oxidized species respectively (Deisingh et al., 2004). In case of a po-

tentiometric electronic tongue, the activity of the reduced species equals

one.

If there are interfering ions, the Nikolsky equation is used:

E = Eo + (RTziF

) · ln[ai + Kij(aij)zi/zj ] (2.3)

where Kij is the selective coefficient of the ion-selective membrane to the

primary ion i in the presence of an interfering ion j and Zi and Zj are the

charges of respectively the primary and interfering ions (Legin et al., 2002b;

Eggins, 2002; Pearce et al., 2003).

0

50

100

150

200

250

300

350

Pot

entia

l (m

V)

Time

Figure 2.2: Typical signal of potentiometric sensor.

A typical potentiometric signal is shown in Figure 2.2. The cycle followed


in the ET measurement protocol is shown by subsequent high potentials

when the sensors are submerged in the sample and low potentials when they

are immersed in a cleaning solution.

Reference electrodevref

vDS

Ion selective membranes dg

Figure 2.3: Schematic diagram of ISFET (s: source, d: drain, g: gate) (after

Pearce et al. (2003)).

In the early 1970s, ion-selective field effect transistors (ISFET’s) were

developed, in which the ion-selective material is directly integrated with

solid-state electronics. A schematic diagram of an ISFET is shown in Figure

2.3. The current between the drain and source (IDS) depends on the charge

density at the semiconductor surface. This is controlled by the gate poten-

tial, which in turn is determined by ions interacting with the ion-selective

membrane. In the ISFET, the normal metal gate is replaced with the refer-

ence electrode and sample solution (Wang, 2006). An attractive feature of

ISFET’s is their small size and ability to be directly integrated with micro-

electronics. Furthermore, if mass fabricated, they can be made very cheaply.

These features make them especially valuable for use in ET’s. Potentiomet-

ric devices offer several advantages for use in ET’s or taste sensors. There

are a large number of different membranes available with different selectivity


properties, such as glass membranes and lipid layers. A disadvantage is that

the technique is limited to the measurement of charged species only (Pearce

et al., 2003).

In voltammetric techniques, the electrode potential is used to drive an

electron transfer reaction and the resulting current is measured. The size of

the electrode potential determines whether the target molecules will receive

or donate electrons. The reaction taking place at the electrode surface is:

O + ne− −→ R (2.4)

where O is the oxidized form and R is the reduced form of the ana-

lyte. At standard conditions, this redox reaction has the standard potential

Eo. The potential of the electrode, can be used to establish a correlation

between the concentration of the oxidized and the reduced form of the an-

alyte, according to the Nernst relation. Pulse voltammetry is of special

interest due to its great sensitivity. Two types of pulse voltammetry are

commonly used: large amplitude pulse voltammetry (LAPV) and small am-

plitude pulse voltammetry (SAPV) (Pearce et al., 2003). At the onset of

a voltage pulse, charged species and oriented dipoles will arrange next to

the surface of the working electrode, forming a Helmholz double layer. A

charging current will flow as the layer builds up. The redox current from

electroactive species is initially large when compounds close to the electrode

surface are oxidized or reduced, but decays with time when the diffusion

layer spreads out. Voltammetric methods can thus measure any chemical

species that is electroactive. Voltammetric methods provide high sensitiv-

ity, a wide linear range and simple instrumentation (Winquist et al., 1997;

Wang, 2006).

Most ET or taste sensors are based on potentiometry or voltammetry.

There are, however, also some other techniques that are interesting to use

and which have special features making them useful for ET’s, such as optical

techniques or techniques based on mass sensitive devices. These types of

devices are very general and have a wide potential for detecting a large


number of different compounds (Eggins, 2002; Pearce et al., 2003).

2.3.3 Instrumentation

Three groups dominate the research on ET technology: Toko and co-workers

in Japan, Winquist and co-workers in Sweden and Legin and co-workers

in Russia. The most important characteristics of all three ET’s and the

commercially available ASTREE ET by Alpha M.O.S. (Toulouse, France)

are discussed below.

The first multisensor system for liquid analysis based on a non-specific

sensor approach is the taste sensor introduced in 1990 by Toko (Toko,

1998a,b, 2000a,b). The multichannel taste sensor is based on ion-sensitive

lipid membranes, immobilized on a PVC polymer. In the taste sensor, five

different lipids are used (n-decyl alcohol, oleic acid, dioctyl phosphate (bis-

2-ethylhexyl)hydrogen phosphate, trioctylmethyl ammonium chloride and

oleylamine) together with their mixtures (Pearce et al., 2003). In total eight

different membranes are used in the ET, where each electrode consists of a

Ag wire, with deposited AgCl, inside a 100 mM KCl solution (Toko, 1998a).

The voltage between a given electrode and a Ag/AgCl reference electrode is

measured. The taste sensor is used to study the five basic taste attributes:

sweetness (sucrose), sourness (HCl), saltiness (NaCl), bitterness (quinine)

and umami (monosodium glutamate). The largest responses were obtained

from the sour and bitter compounds, followed by umami and salty. For

sucrose almost no response was obtained (Toko, 2000a). The multichannel

system has been commercialized as the taste sensing system SA402 (An-

ritsu Corp., Japan) (Ivarsson et al., 2001). The detection element is an

eight-channel multisensor, placed on a robot arm and controlled by a com-

puter. The samples are placed in a sample holder together with a cleaning

solution and reference solutions. After cleaning the multisensor in a cleaning

solution, it is inserted in the sample solution. Every couple of hours, the

multisensor is placed in the reference solution for calibration purposes.

A second type of ET was developed at the Swedish Sensor Centre, S-


SENCE, at Linkoping University. The first voltammetric ET is based on

both LAPV and SAPV applied to a double working electrode, an auxiliary

and a reference electrode (Winquist et al., 1997). The double working elec-

trode consisted of one wire of Pt and Au. After further development in the

last decade, the most recent configuration consists of five working electrodes,

a reference electrode and an auxiliary electrode of stainless steel. Metal wires

of Au, Ir, Pd, Pt, and Rh used as working electrodes are embedded in epoxy

resin and placed around a reference electrode. The sensor is inserted in

a plastic tube ending with a stainless steel tube as an auxiliary electrode.

Different types of pulsed voltammetry can be applied, LAPV, SAPV and

staircase (Pearce et al., 2003). A hybrid ET has also been developed, based

on the combination of potentiometry, voltammetry and conductivity. The

hybrid ET consists of six working electrodes of different metals (Au, Ir,

Pd, Pt, Re and Rh), three ion-selective electrodes and a Ag/AgCl reference

electrode. The measurement principle is based on LAPV in which current

transients are measured (Winquist et al., 2000).

Figure 2.4: Electronic tongue developed at Saint-Petersburg University.

The third type of ET was developed at the University of Saint-Petersburg


(ETSPU) in Russia (Figure 2.4). The main features of the sensors used in the

ET are their non-specificity and wide and reproducible cross-sensitivity to

different components in liquid media (Legin et al., 1997; Vlasov et al., 2000,

2002). The sensing materials include a wide range of chalcogenide glasses

doped with different metals, plasticized polyvinylchloride (PVC) polymers

containing various plasticizers and active substances such as ionophores,

neutral carriers, metalloporphyrins and crystalline compositions. The sensor

array typically comprise from 10 to 30 sensors, depending on the application.

Potentiometric measurements with the sensor array are commonly made

relative to the Ag/AgCl reference electrode, by use of a high input impedance

interface device (Vlasov et al., 2002; Legin et al., 2003). More information

on the chalcogenide ion selective electrodes is available in US patent 5464511

(Vlasov and Bychkov, 1995).

Figure 2.5: ASTREE electronic tongue developed by Alpha M.O.S.

During the nineties, Alpha M.O.S. (Toulouse, France) successfully devel-

oped electronic noses and tongues for the measurement of aroma and taste.

The ASTREE Liquid and Taste Analyzer (Figure 2.5) is made out of seven

sensors for liquid analysis, which are available in two different sets, with


a cross-selectivity to dissolved organic compounds in liquids (AlphaM.O.S.,

2006). The sensors are made from silicon transistors with an organic coating

that governs sensitivity and selectivity of each individual sensor. The differ-

ence between each sensor and a Ag/AgCl reference electrode is measured.

The system can be fully automated through a 16-position autosampler (Tan

et al., 2001). Since this ET is a commercial product, no details are available

on the sensor array (US patent 6290838) (Mifsud and Lucas, 2003).

2.3.4 Applications

The taste sensor developed by Toko has been applied for the quantification

of taste. The sensitivity of the taste sensor was studied in aqueous solutions

of the five basic tastes (Toko, 1998a). The researchers focused on tasting

umami and bitter substances. Suppression of bitter taste by the presence of

sweet substances, often used to mask bitter taste of drugs, has been studied

using the taste sensor (Takagi et al., 2001). An attempt was made to build

a taste map by expressing the tastes of food products combining the basic

tastes using the ET. Furthermore, the taste sensor was applied to tomato

juices (Kikkawa et al., 1993), commercial brands of sake (Iiyama et al., 2000),

brands of beer, coffee from different origins, commercial brands of mineral

water, milk with different kinds of heat treatment and wine from different

vineyards (Toko, 1998a,b, 2000b).

The voltammetric ET has been applied for the analysis of different food

products. Different beverages were studied using the first version of the ET:

nine brands of orange juice, two types of orange soft drinks, apple juice, tea,

drinking water and pasteurized milk (Winquist et al., 1997; Ivarsson et al.,

2001; Winquist et al., 2005). It was found that the ET could distinguish be-

tween different types of beverages. Ageing of beverages was evaluated using

the same sensor array. The voltammetric ET with five working electrodes

has been used to monitor milk souring and bacterial growth (Winquist et al.,

1998). The hybrid ET combining voltammetry with six working electrodes,

two ion-selective electrodes, a CO2 electrode and conductivity was applied

for the recognition of fermented milk (Winquist et al., 2000). This ex-


periment proved that the combination of voltammetric, potentiometric and

conductometric signals improves the performance of the ET.

The ETSPU has shown its potential in both quantitative measurements

and classification. The sensitivity of more than 40 different sensing materials

to organic taste substances, present in many food products, has been eval-

uated (Vlasov et al., 2002). For each class of taste substance sensors with a

meaningful and reproducible response were identified. The ET was applied

to quantitative and qualitative analysis of numerous products. Recognition

of mineral waters has been described (Legin et al., 1999b, 2000). Some

mineral waters were deliberately polluted by organic matter and the con-

tamination was detected by means of the ET. The ET was applied to dis-

criminate between fruit juices and to monitor juice spoilage (Rudnitskaya

et al., 2001). Milk samples subjected to different heat treatments and from

different manufacturers were studied qualitatively and differences in sour-

ing between UHT and pasteurized milk were detected using the multisensor

system (Legin et al., 1997). The ability of the ET to distinguish between

regular and diet cokes and experimental coke mixtures has been evaluated

(Legin et al., 2002a). The results were correlated to sensory panel scores

in a preference mapping study. Analysis of Italian wine showed that the

ET could discriminate between wines from different origins. The ET could

determine the alcohol and organic acid content of wine, together with the

total acidity and pH (Legin et al., 1999b).

The ASTREE, in combination with the electronic nose developed by

Alpha M.O.S., was used to classify food and beverages, determine bitterness

in coffee (AlphaM.O.S., 2006) and predict 34 sensory characteristics of apple

juice, like aroma, taste, color, flavor, mouthfeel, aftertaste, etc. (Bleibaum

et al., 2002).

24 2.4 Fourier transform infrared spectroscopy

2.4 Fourier transform infrared spectroscopy

2.4.1 Introduction

In 1800 Sir William Herschel, an astronomer, made the important discovery

of infrared (IR) light in the first experiment which showed there were forms of

light invisible to the human eye. The development of FTIR would have been

impossible without the discovery of this IR light and the development of the

Michelson interferometer. This optical device was invented in 1880 by Albert

Abraham Michelson, who was awarded the Noble Prize in Physics in 1907 for

accurately measuring wavelengths of light using his interferometer. At first

interferograms were measured manually, but the invention of the computer

and the fast Fourier transform (FFT) by J.W. Cooley and J.W. Tukey were

the breakthrough that made FTIR possible. The first commercially available

FTIR spectrophotometers were manufactured at the Digilab of Cambridge

University (Massachusetts, USA) due to the efforts made by P. Griffiths,

R. Curbelo, L. Mertz and many others. The first instruments made the

acquisition of qualitative high resolution data in a short period of time and

established the advantages of FTIR over previous means of obtaining IR

spectra (Smith, 1996).

In the past, the mid-IR region (4000 cm−1-400 cm−1) has been of min-

imal interest to the food analysist. Most foods contain large amounts of

water that strongly absorbs mid-IR radiation. The poor transmission and

often high scattering of many samples means that conventionally very little

light can be detected. The development of FTIR spectroscopy has renewed

interest in the potential of mid-IR for food analysis. FTIR spectrometers

using an interferometer provide more energy to the sample, scan a lot faster

and have the capability to co-add data so that within a short time spectra

can be produced from poorly transmitting samples with acceptable signal-

to-noise ratios. Mid-IR has significant advantages over near-IR for spectral

assignment, resolution and ease of quantification. Another advantage is

that mid-IR spectra can provide information about the physical and chemi-

cal states of individual compounds (Wilson and Goodfellow, 1994; Chalmers


and Griffiths, 2002; Griffiths and de Haseth, 2007). The theoretical back-

ground of FTIR will be discussed in the next part.

2.4.2 Theory

Mid-IR spectroscopy involves the molecular absorption of radiation between

4000 cm−1-400 cm−1. Figure 2.6 shows the different parts of the spectral

region, indicating the position of the IR region. IR spectra result from tran-

sitions between quantized vibrational energy states. Molecular vibrations

can range from the simple coupled motion of the two atoms of a diatomic

molecule to the much more complex motion of each atom in a large poly-

functional molecule. Molecules with N atoms have 3N degrees of freedom,

of which three represent translational motion in mutually perpendicular di-

rections and three represent rotational motion around the axes of inertia.

The remaining 3N -6 degrees of freedom give the number of ways that the

atoms can vibrate in a nonlinear molecule. The energy difference for transi-

tions between the ground state (ν = 0) and the first excited state (ν = 1) of

most vibrational modes corresponds to the energy of radiation in the mid-

IR spectrum. Figure 2.7 shows the potential energy of a diatomic molecule

as a function of the distance between the atoms. Simple harmonic motions

obey Hooke’s law, while anharmonic motions in practice show a Morse type

potential function (Griffiths and de Haseth, 2007).

The absorption arises through transitions between vibrational and rota-

tional energy states of the molecule. Different types of molecular vibrations

are shown in Figure 2.8. Since the absorption bands depend on the masses

of the atoms and the force constant of the vibrating bonds, it is possible

to assign absorptions to specific chemical entities. Furthermore, the inten-

sity of the absorption is proportional to the concentration of the absorbing

species. This makes IR spectroscopy a very useful technique for qualitative

and quantitative analysis.


Fig

ure

2.6

:S

pec

tralre

gio

ns

ran

gin

gfr

om

NM

Rtoγ

-ray

(Gri

ffith

san

dd

eH

ase

th,

2007

).


r

V(r

)

0

Harmonic potential (Hooke’s law)

Anharmonic potential (Morse type)

ν = 0ν = 1ν = 2

Figure 2.7: Potential energy of a diatomic molecule during vibration for a har-

monic oscilator (dashed line) and an anharmonic oscilator (solid line) (after Griffiths

and de Haseth (2007)).

The basis of most quantitative analysis in optical techniques is the Beer-

Lambert relationship:

I = I0 · 10−εcl (2.5)

where I is the light intensity measured by the detector after passing

through the sample, I0 is the incident light intensity, ε is the molar extinc-

tion coefficient, c is the chromophore concentration and l is the light path

length through the sample. For quantitative analysis, often, IR spectra are

presented in absorbance units (A) defined as

A = − log10

II0

= εcl (2.6)

In the mid-IR well-resolved bands can often be identified as originating


+ + + -

(a) Stretching vibrations

(b) Bending vibrations

Symmetric Asymmetric

In-plane rocking

Out-of-plane wagging

In-plane scissoring

Out-of-plane twisting

Figure 2.8: Stretching and bending vibrations (after Griffiths and de Haseth

(2007)).


from specific compounds, so that the Beer-Lambert relationship sometimes

can be used directly in FTIR. In complex mixtures, however, simple Beer-

Lambert relationships cannot be used. In such cases highly overlapping

bands may be present so that the absorbance at a given wavelength no

longer arises from a single component. Multivariate analysis can offer a

solution (Griffiths and de Haseth, 2007).

2.4.3 Instrumentation

2.4.3.1 Michelson interferometer

The main part of a FTIR spectrometer is a Michelson interferometer (Figure

2.9). An interferometer consists of a fixed mirror, a movable mirror and a

beam splitter. An IR beam radiated by a source is divided at the beam

splitter. Half of the light is send to a fixed mirror and half is reflected onto

a moving mirror. The two light beams are returned to the beam splitter

where they are combined and directed to the sample and the detector. If

the two mirrors are at equal distance from the beam splitter, the light is in

phase, resulting in constructive interference (Gunasekaran, 2001). Maximum

energy is then redirected by the beam splitter. If the movable mirror is

moved a distance l (mm), a path length difference δ (mm) is introduced in

one part of the interferometer. For a monochromatic light source destructive

interference occurs when δ = (n + 1/2)λ, where λ is the wavelength of the

light (µm). Constructive interference is found when δ = λ, 2λ, 3λ, ...,nλ. As

the mirror is moved, a fluctuating cosine wave, called an interferogram, is

seen as detector output. P(δ), the energy detected at the detector is given

by

P(δ) = B(σ) cos 2πδσ (2.7)

where B(σ) takes imperfections in the optical design into account, δ is

the path length difference and σ is the wavenumber of the light (cm−1), with

σ = 1/λ.


Direction of travel

Movable mirror

Fixed mirror

Detector

Source

Beamsplitter

Figure 2.9: Schematic view of Michelson interferometer (after Griffiths and

de Haseth (2007)).

In a true IR source many frequencies are present and the interferogram

is the sum of an infinite number of cosine waves, so that

P(δ) =∫ +∞

0B(σ) cos 2πδσ.dσ (2.8)

An interferogram contains all information of a spectrum but it is not

easy to interpret. A Fourier transform of the data provides a spectrum,

which shows the variation of the intensity as a function of the wavenumber

(Equation 2.9). In FTIR an interferogram is thus collected and transformed

into a spectrum.

P(σ) =∫ +∞

−∞B(δ) cos 2πδσ.dδ (2.9)


where P(σ) is the energy incident at the interferometer (Wilson and

Goodfellow, 1994).

2.4.3.2 Attenuated total reflectance

The development of FTIR has led to an increased interest in sample pre-

sentation techniques. Today there is a wide choice of sample accessories

available with different designs and approaches. The main categories are

transmission methods, internal reflectance, diffuse reflectance, photoacous-

tic detection, Raman spectroscopy and GC/IR (Gunasekaran, 2001; Griffiths

and de Haseth, 2007). Since internal reflection is used in the experimental

part of this thesis, an overview of the theoretical background is given next.

Internal reflection, also known as attenuated total reflectance (ATR), is

one of the most powerful FTIR methods because of its flexible sample pre-

sentation. ATR accessories are available in many configurations for specific

applications. The internal reflectance element is the main component of

an ATR cell. In most cases it is a prism of IR transmitting material with

a high refractive index. An overview of commonly used ATR materials is

given in Table 2.1. Factors influencing the choice of ATR crystal include

the spectral range, refractive index of the crystal material and the sample,

pH range, angle of incidence and efficiency of sample contact (Griffiths and

de Haseth, 2007).

Horizontal ATR (HATR) accessories comprise a parallelogram prism

with mirrored ends. A spectrum can be acquired when a sample is spread

over the crystal (Figure 2.10). When light passes from one medium to an-

other the angle at which the radiation is refracted is described by Snellius’

law. The prism face is cut at an angle such that the light passes into the

prism at a predetermined angle. This angle θi is larger than the critical

angle θc given by

θc = sin−1 n2

n1(n1 > n2) (2.10)


Table

2.1

:O

pti

cal,

mec

han

ical

an

dch

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al

pro

per

ties

of

com

monly

use

dA

TR

cryst

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ater

ials

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Mat

eria

lR

efra

ctiv

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pHC

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ents

Typ

ical

sam

ples

inde

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(kg.

mm−

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(cm−

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ZnS

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4020

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650

120

5-9

Mos

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high

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solid

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and

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mon

d2.

4130

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5700

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d,sc

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mat

eria

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rdpo

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/ZnS

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itab

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plic

atio

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ing

and

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fficu

ltto

aw

ide

rang

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chem

ical

s.an

alyz

eso

lidsa

mpl

es.

The

mos

tex

pens

ive

AT

Rm

ater

ial.

AM

TIR

2.50

1100

0-72

517

01-

9Se

leni

um,

arse

nic

&ge

rman

ium

glas

s.O

rgan

icso

lven

ts,

past

es,

gels

,

Rel

ativ

ely

hard

,br

ittl

e,so

ftpo

lym

ers

and

pow

ders

,

may

beda

mag

edun

der

sam

ples

cont

aini

ngw

ater

.

unev

enpr

essu

re.

Goo

dfo

rw

orki

ngw

ith

acid

icsa

mpl

es.


where n2 is the refractive index of the surrounding medium and n1 is the

refractive index of the prism material (Chalmers and Griffiths, 2002).

MirrorInput beam Output beam

Trough

Internal reflection element

Figure 2.10: Schematic view of horizontal ATR accessory (after Griffiths and

de Haseth (2007)).

When θi > θc, total internal reflectance occurs at the interface. A num-

ber of reflections occur in the crystal before the light leaves the crystal. At

each point of internal reflection an evanescent wave is produced and some

radiation penetrates into the surrounding medium and can interact with

this medium. Attenuation of the reflected light results when the medium

absorbs IR light. The number of reflections and the depth of penetration

at each reflection (dp) determines the effective path length of the ATR cell.

The number of reflections, typically between 1 and 10, is determined by θi

and the dimensions of the prism. The penetration depth is given by


dp =λ

2πn1[sin2 θi − (n2/n1)2]12

(2.11)

where λ is the wavelenght of the light (Wilson and Goodfellow, 1994).

Figure 2.11: Tensor 27 FTIR and AMTIR ATR cell.

2.4.3.3 Flow injection analysis

Flow injection analysis (FIA) is a well established flow based technology

that brought speed, automation of solution handling, miniaturization and

low cost to the analytical laboratory during the last 30 years. The technique

was first applied by Ruzicka en Hansen in Denmark in 1974 (Ruzicka and

Hansen, 1975). Since then FIA has undergone some changes and modifica-

tions which can be classified as three generations: (i) FIA, (ii) sequential

injection analysis (SIA) and (iii) bead injection (BI) - lab-on-valve (LOV)

(Hansen and Wang, 2004).

The first generation of FIA was defined as ’the sequential insertion of

discrete sample solution into an unsegmented continuous flowing stream with


subsequent detection of the analyte’ by Ruzicka and Hansen (1975). In the

simplest form of FIA the sample is injected into a continuous flow of a

reagent solution, dispersed and transported to a detector, a set-up which

shows some similarity with chromatography. The advantage of FIA is that

any number of additional lines with reagents can be added and any type of

detector can be used (Hansen and Wang, 2004).

In the second generation flow injection analysis has evolved into SIA.

This technique allows miniaturization of the set-up with as a consequence

a reduction of the consumption of sample and reagent solutions and hence

leads to generation of minute amounts of waste (Gallignani and Brunetto,

2004). While most FIA procedures use continuous and uni-directional pump-

ing of carrier and reagent streams, SIA is based on using programmable

bi-directional discontinuous flow, precisely controlled by a computer. Flow

programming is a unique tool that makes solution handling in the micro

scale possible (Ruzicka and Marshall, 1990). SIA has been applied to the

analysis of a wide variety of analytes in matrices as diverse as food, bever-

ages, bioprocesses, environmental, pharmaceutical and industrial processes

(Hansen and Wang, 2004). A general schematic flow diagram of a sequen-

tial injection analyzer is shown in Figure 2.12. The basic components of

the system, a pump with only one carrier stream, a single selection valve, a

single channel and a detector, are indicated (van Staden and Stefan, 2004).

In this work, an flow injection system based on the principles of SIA will be

developed.

The third generation of FIA, LOV, has many of the features of SIA. Here,

an integrated microconduit is placed on top of the selection valve. This

microconduit is designed to handle all the necessary operations required for

a given assay and thus acts as a small laboratory. LOV with integrated

BI has proven to be an attractive methodology in many contexts. BI uses

programmable flow to handle precise volumes of suspended microbeads that

serve as a carrier for reagents or analytes. It allows automated renewal of

the solid state phase, which is a critical feature when assay surfaces become

contaminated or dysfunctional, such as immunoassays (Ruzicka, 2000; Wang

and Hansen, 2003).


Figure 2.12: Schematic flow diagram of a basic sequential injection analyser. S

= sample; R = reagent; SV = selection valve; HC = holding coil; RC = reaction

coil; D = detector.

2.4.4 Applications

ATR-FTIR has proved to be a very attractive sampling tool, since many

applications are reported in literature. Applications are not only found in

the characterization of food products and beverages, but also in other fields

such as determination of products used for art purposes (Peris-Vicente et al.,

2007), medical research (Liu and Webster, 2007; Xu et al., 2007), fermenta-

tion monitoring (Roychoudhury et al., 2006), quality control and determina-

tion of pesticides (Armenta et al., 2005b,c; Khanmohammadi et al., 2007),

determination of surfactants in shampoo and soap (Carolei and Gutz, 2005),

quality control in PVC manufacturing (Bodecchi et al., 2005) and design of

food packaging (Irudayaraj and Yang, 2002; Lagaron et al., 2007). Both

qualitative and quantitative studies involving food products have been per-


formed using ATR-FTIR. Many examples have been reported using various

food products ranging from fruit and vegetable (Garrigues et al., 2000; Ar-

menta et al., 2005a; Dogan et al., 2007) to their manufactured products

like fruit juices and oils (Inon et al., 2003; He et al., 2007). ATR-FTIR

has also widely been used for the analysis of beverages like beer and wine

samples (Edelmann et al., 2003; Moreira and Santos, 2004) and other drinks

(Paradkar and Irudayaraj, 2002; Irudayaraj and Tewari, 2003; Moros et al.,

2005).

FIA combined with ATR-FTIR has been applied successfully for the

determination of diverse analytes in different liquid matrices (Lendl and

Kellner, 1995; Daghbouche et al., 1997; Ayora-Canada and Lendl, 2000).

The use of enzyme reactors has been introduced in FIA in combination with

ATR-FTIR and is applied in the same fields.

2.5 Multivariate analysis techniques

Multivariate data analysis refers to any statistical technique used to analyze

multiple responses. Multivariate techniques are used in analysis to perform

studies across multiple dimensions while taking into account the effects of

all variables on the responses of interest (Sharma, 1996). Two types of

data should be analyzed using multivariate statistics: high dimensional data

(e.g. spectral data) with strong correlations between variables and data of

which different variables are measured individually (e.g. HPLC and sensory

analysis). In this part, first, different data pretreatment techniques which

are applied in IR spectroscopy are discussed (Martens and Naes, 1998; Naes

et al., 2004). Next, multivariate techniques dealing with both classification

and quantification are briefly discussed.

38 2.5 Multivariate analysis techniques

2.5.1 Data pretreatment

2.5.1.1 Averaging and centering

By averaging over samples, in case of replicates, or over variables, of the

same sample, the data are shown as their best estimation. When data are

mean centered, the average is subtracted from each variable. All results are

then interpretable in terms of variation around the mean (Naes et al., 2004).

2.5.1.2 Smoothing

Smoothing techniques include moving average filters and the Savitzky-Golay

algorithm to remove noise from IR spectra. Both techniques improve the

visual aspects of the spectra, but can remove information while it is not

clear yet whether this information is useful (Naes et al., 2004).

2.5.1.3 Standardization

Standardization is performed by dividing the spectrum at every wavelength

by the standard deviation of the spectrum at this wavelength. Typically,

variances of all wavelengths are standardized to 1, which results in an equal

influence of the variables in the model (Naes et al., 2004).

2.5.1.4 Normalization

Multiple scatter correction (MSC) is one of the most popular normaliza-

tion techniques. It is used to compensate for additive and multiplicative

effects in the spectral data, which are induced by physical effects such as

the non-uniform scattering throughout the spectrum. The method attempts

to remove the effects of scattering by linearizing each spectrum to some ideal

spectrum of the sample, which usually corresponds to the average spectrum.

Extended multiple signal correction (EMSC) is an extension of MSC and al-

lows to compensate also for chemical interference effects by incorporating


known spectra of the interferents and analytes (Martens and Stark, 1991).

In standard normal variate correction (SNV), each individual spectrum is

normalized to zero mean and unit variance (Martens and Naes, 1998).

2.5.1.5 Transformations

Taking a first or second derivative can solve problems of baseline shifts and

superposed peaks. Derivative spectra of first order correct for additive ef-

fects. Derivative spectra of second order are very popular as they correct for

both additive and multiplicative effects. Both derivatives are usually calcu-

lated according to the Savitzky-Golay or Norris algorithm. The parameters

of the algorithm should be selected carefully in order to avoid amplification

of spectral noise (Martens and Naes, 1998; Naes et al., 2004).

2.5.2 Principal component analysis

Principal component analysis (PCA), an unsupervised technique, is one

of the most often used chemometric methods for data reduction and ex-

ploratory analysis on high dimensional data sets. The main goal of PCA is

to obtain a small set of principal components (PC) that are linear combina-

tions of the original variables and contain most of the variability on these

data sets. The first PC accounts for the maximal variance in the data, the

second PC accounts for the maximal variance that has not been accounted

for by the first PC and so on. The new subspace defined by these PC’s

leads to a model that is easier to interpret than the original data set, since

only a few PC’s need to be used to represent the multivariate data structure

rather than all variables. This technique is highly suitable for data explo-

ration (reduction of variables) and quality control (process control) since

data visualization is made easy using score plots and correlation loadings

plots (Sharma, 1996).


2.5.3 Principal component regression

Principal component regression (PCR) is a two-step procedure which first

decomposes the X-matrix using a PCA and then fits a multiple linear re-

gression (MLR) model, using a small number of PC’s instead of the original

variables as predictors. Usually a small number of uncorrelated PC’s is suf-

ficient. A drawback is that the PC’s are ordered according to decreasing

explained variance of the spectral matrix and that the first PC’s which are

used for the regression model are not necessarily the most informative with

respect to the Y-variable.

2.5.4 Partial least squares

Partial least squares regression (PLS) is a procedure that generalizes and

combines features from principal component analysis and multiple regression

and is used to model the relationship between a set of predictor variables

(X) and a set of response variables (Y) with explanatory or predictive pur-

pose. The Y-variables are actively used to estimate the partial least squares

components (PC’s) to ensure that the first components are those that are

most relevant for predicting the Y-variables. The interpretation of the re-

lationship between the X-data and Y-data is simplified as this relationship

is concentrated on the smallest possible number of PC’s. A full description

of the NIPALS algorithm used for PLS analysis is given by Trygg and Wold

(2002). The method performs particularly well when the various X-variables

express common information, i.e. when there is a large amount of correla-

tion, or even co-linearity, which is the case for spectral data (Geladi and

Dabakk, 1995).

2.5.4.1 PLS1 and PLS2

There are two versions of the PLS algorithm: PLS1 and PLS2. The differ-

ences between these methods are subtle but have very important effects on

the results. In PLS1, a separate model is calculated for each constituent of


interest. In this case, the separate sets of eigenvectors and scores are specif-

ically tuned for each constituent. PLS regression can be easily extended to

the simultaneous prediction of several quality attributes. The algorithm is

then called PLS2. In PLS2, the calculated vectors are not optimized for

each individual constituent, but for all constituents simultaneously. This

may sacrifice some accuracy in the predictions of the constituent concen-

trations, especially for complex sample mixtures. The speed of calculation,

however, is an advantage of PLS2. Since no separate set of eigenvectors and

scores must be generated for every constituent of interest, the calculations

will be less time consuming than in PLS1 (Martens and Naes, 1998; Naes

et al., 2004).

2.5.4.2 Partial least squares-discriminant analysis

Partial least squares discriminant analysis (PLS-DA), a supervised tech-

nique, is used to classify samples. PLS-DA is a regression extension of PCA

that takes advantage of class information to maximize the separation be-

tween groups of observations. The technique consists in a classical PLS

regression where the response variable is a categorical variable, which is re-

placed by the set of dummy variables describing the categories, expressing

the class membership of the sample. PLS-DA does not allow other response

variables than the ones that define the groups of samples. As a consequence,

all measured variables play the same role with respect to the class assign-

ment. PC’s are built to find a compromise between two purposes: describing

the set of explanatory variables and predicting the response variables (Naes

et al., 2004).

2.5.4.3 Model validation and accuracy

In order to assess the accuracy of the calibration model and to avoid over-

fitting, validation procedures have to be applied. Leverage correction is an

equation based procedure to estimate the prediction accuracy without per-

forming any prediction and is to be avoided at all times because it always


leads to overoptimistic estimates. In leave-one-out cross validation, one sam-

ple is removed from the dataset and a calibration model is constructed for

the remaining subset. The removed samples are then used to calculate the

prediction residual. The process is repeated with other subsets until every

sample has been left out once and in the end the variance of all prediction

residuals is estimated. In multifold cross validation, a well-defined number

of samples are left out of the calibration set instead of one. The model is

validated using the left-out samples (Martens and Naes, 1998).

In internal validation, the dataset is split into a calibration and a vali-

dation set. The calibration model is constructed using the calibration set,

and the prediction residuals are then calculated by applying the calibration

model to the validation set. In external validation, the validation dataset

is independent from the calibration dataset (Martens and Naes, 1998; Naes

et al., 2004).

Prediction models having a correlation between the predicted and mea-

sured value of a variable above 0.90 are considered to be excellent, if the

slope and offset are close to 1 and 0 respectively. The correlation, slope and

offset give information about the quality of the model, but give no direct

information about the prediction accuracy. The root mean square error of

cross validation (RMSECV) (Equation 2.12) and ratio of prediction to devi-

ation (RPD) (Equation 2.13) values are often used to assess the performance

of a model to predict a variable (Geladi and Dabakk, 1995).

RMSECV =

√√√√ 1n

n∑i=1

(ci − ci)2 (2.12)

where ci is the known concentration, ci is the predicted concentration

and n is the total number of samples for the cross validation dataset.

RPD =St.Dev.

RMSECV(2.13)

where St.Dev. is the standard deviation of all samples for the compound

studied. An RPD value below 1.5 indicates that the model is not usable.


An RPD value between 1.5 and 2 reveals a possibility to distinguish between

high and low values, while a value between 2 and 2.5 makes approximate

quantitative predictions possible. For values between 2.5 and 3, and above

3, the prediction is classified as good and excellent, respectively (Saeys et al.,

2005).

Chapter 3

Electronic tongue technology

3.1 Introduction

Since flavor is an important quality attribute, there is a need for rapid, low

cost and simple methods of flavor analysis and consumer quality assessment.

After intensive research a first rapid multisensor system for aroma analysis

was developed in the beginning of the 1980s (Gardner and Bartlett, 1994).

Similar to these arrays of sensors for the analysis of gases, called electronic

noses (EN), arrays of sensors for liquid analysis were developed in the last

decade. Electronic tongues (ET’s) are defined as ’multisensor systems for

liquid analysis based on chemical sensor arrays’ by Legin et al. (2002b).

Although the development of ET’s is still in its early stages, they have

proved to be successful in discrimination and classification of food prod-

ucts, food quality evaluation, control monitoring. The main advantages of

ET’s are the low cost, easy-to-handle measurement set-up and speed of the

measurements (Vlasov et al., 2002; Deisingh et al., 2004). In fruit quality

research, the ET has been used successfully for the classification and deter-

mination of fruit juices (Legin et al., 1997; Bleibaum et al., 2002; Gallardo

et al., 2005). Legin et al. (1997) used their system for the discrimination

between various sorts of the same type of beverages and the monitoring of

the aging process of juices. Orange based drinks were classified using a po-

45

46 3.1 Introduction

tentiometric ET, showing possibility to determine the natural juice content

of the samples (Gallardo et al., 2005). Research was conducted to compare

apple juice quality evaluated by consumers, a trained sensory panel and in-

strument analysis using the ASTREE ET (Alpha M.O.S., Toulouse, France)

and the Prometheus EN (Alpha M.O.S., Toulouse, France) (Bleibaum et al.,

2002).

Until now no extensive study has been published on the use of ET tech-

nology in high throughput experiments on horticultural produce. The ET

could be a promising technique in large scale experiments. In breeding or

cultivar selection programs, large amounts of fruit need to be analyzed in a

very short time period. Due to its measurement speed, ET technology could

be applicable in online analysis during these experiments. Also, there are no

published data about the performance of different types of ET’s. Neverthe-

less, such information is crucial for the applicability of ET’s to the analysis

of fruit.

The objective of this chapter is to study the potential of ET’s as rapid

techniques for the high throughput analysis of taste compounds in fruit and

vegetable. Hereto three experiments will be carried out.

� In a first experiment, the potential of the ET developed at Saint-

Petersburg University (ETSPU) to classify apple and tomato cultivars

according to their sugar and acid profile and to quantify their chemical

content will be studied. Hereto, first, the information content of the

ETSPU is compared to that of the HPLC reference technique. Next,

the ability of the ETSPU to predict the chemical composition of the

apples and tomatoes is investigated.

� In a second experiment, the potential ETSPU will be compared to the

commercially available ASTREE ET of Alpha M.O.S. for rapid qual-

itative and quantitative determination of taste compounds in Belgian

tomato cultivars. The samples were, next to the ET, also analyzed us-

ing sensory panels. The results of these panel measurements in relation

to the ET results will be discussed in Chapter 6.

Electronic tongue technology 47

� In a final experiment, the ability of the multisensor system as a tool

for quality control in food industry will be evaluated, since errors in

the production process of all food products should be detected as soon

as possible. Different multifruit juices and the individual syrups they

are composed of are analyzed using the ETSPU. The ability of the ET

to detect differences in the fruit composition of the multifruit juices

will be studied.

This chapter is divided in four main sections. In Section 3.2 the materials

and methods are described. The results of the experiments with tomatoes,

apples and fruit juices are given in Section 3.3. First, the results of the

classification and quantification experiment using apples and tomatoes are

shown. Second, the results of the experiment in which the comparison of

two types of ET’s is made are presented. Finally, the results of the quality

control experiment of fruit juices are shown. In Section 3.4 the results

are discussed and compared to literature findings. Concluding remarks are

formulated in Section 3.5. The results presented in this chapter have been

published by Beullens et al. (2004, 2005a,b, 2006a, 2007a,b, 2008b,a), Legin

et al. (2005a) and Rudnitskaya et al. (2006).

3.2 Materials and methods

3.2.1 Samples

3.2.1.1 Classification of apple and tomato cultivars and quantifi-

cation of taste compounds

An artificial fruit juice was made using pure compounds which determine the

taste of fruit. The basic composition of the artificial juice is shown in Table

3.1. The chemical compounds were purchased at Sigma Aldrich (Steinheim,

Germany). Different concentrations of citric acid, malic acid and glucose

were added to the basic artificial juice for calibration purposes. These three

compounds were chosen based on their importance in tomato (Petro-Truza,

48 3.2 Materials and methods

Table 3.1: Composition of the artificial juice made to calibrate the ETSPU.

Compound Artificial juice

Glucose 11 g/L

Fructose 7 g/L

Sucrose 0.9 g/L

Citric acid 2 g/L

Malic acid 0.8 g/L

Oxalic acid 0.2 g/L

Succinic acid 1 g/L

Tartaric acid 1 g/L

K2HPO4 1900 mg/L

Na2HPO4.12H2O 60 mg/L

CaCl2.2H2O 30 mg/L

MgCl2.6H2O 800 mg/L

KOH 9 mg/L

1987). After preparation of the mixtures, they were frozen in falcon tubes

using liquid nitrogen and frozen at −80 ◦C until analysis.

Apples (Malus× domestica Borkh.) of five cultivars were used in the

first experiment: Cox, Elstar, Golden Delicious, Jonagold and Pinova. The

apples were purchased at the local supermarket. Twenty apples per cultivar

were cut into pieces, put into falcon tubes and frozen in liquid nitrogen. The

frozen samples were stored at −80 ◦C until further sample preparation was

performed.

Four tomato cultivars (Lycopersicon esculentum Mill.) were selected for

this experiment: Aranca, Climaks, Clotilde and DRW 73-29. Twenty toma-

toes per cultivar were harvested at the Proefstation voor de Groenteteelt in

Sint-Katelijne-Waver (Belgium) at ripeness stage 5 (light red class) (USDA,

1975). The tomatoes were stored for one day at ambient atmosphere (18 ◦C

and 80% relative humidity). The day after harvesting the tomatoes were

cut into pieces, put into falcon tubes and frozen in liquid nitrogen. The


performed.


Calibration curves for citric acid, malic acid and glucose where made

with the ETSPU, using the artificial juices with variable concentrations of

the compound of interest. The apple and tomato samples were analyzed

using the ETSPU with 27 sensors, ATR-FTIR (Chapter 4) and HPLC.

3.2.1.2 Comparison of two electronic tongues

Six tomato cultivars (Lycopersicon esculentum Mill.) were selected based on

their difference in taste determined by a sensory panel, which is mainly de-

fined by the difference in sweetness and sourness, to assure a broad range in

acid and sugar content (Buysens, 2006a). The selected cultivars were: Ad-

miro, Macarena, Sunstream, Amoroso, Tricia and Clotilde (Table 3.2). The

fruit were obtained at the fruit- and vegetable Auction of Mechelen (Bel-

gium) and the Auction of Hoogstraten (Belgium). All tomatoes were picked

at ripeness stage 5 (light red class) (USDA, 1975). The fruit were stored

during one day at ambient atmosphere (18 ◦C and 80% relative humidity).

The day after purchase the tomatoes were juiced and the juice of the differ-

ent tomatoes was mixed all together in one large recipient of 10 L. The juice

was then divided over several falcon tubes and frozen in liquid nitrogen.

The samples were stored at −80 ◦C until measurement with the ETSPU,

the ASTREE ET, an enzyme based reference technique (EHT), atomic ab-

sorption spectroscopy (AAS), the optimized sequential injection-attenuated

total reflectance-Fourier transform infrared spectroscopy (SIA-ATR-FTIR)

system (Chapter 5) and sensory analysis (Chapter 6).

Table 3.2: Sweetness and sourness of the six Belgian tomato cultivars as perceived

by a sensory panel (+ high, 0 intermediate, - low).

Cultivar Sweetness Sourness

Admiro 0 0

Macarena - +

Sunstream + -

Amoroso + +

Tricia - -

Clotilde 0 0


3.2.1.3 Quality control of fruit juices

Three multifruit juices of the brand Sunland (Sunnyland, Turnhout, Bel-

gium) were purchased at the local supermarket: Fruitdrink ACE, Benefits

Vitality and Benefits Immunity. The composition of the multifruit juices,

as mentioned on the label, is given in Table 3.3. The individual syrups

and juices used to compose the multifruit juices were provided by Sunny-

land Distribution (Turnhout, Belgium): blend-9 fruit (fixed blend made out

of pineapple, orange, passion fruit, mandarin, grapefruit, banana, mango,

guava and papaya), lemon, orange, passion fruit, apple, red grape, elder-

berry, cherry and strawberry. The juices and syrups were put in falcon

tubes, frozen using liquid nitrogen and stored at −80 ◦C until further sam-

ple treatment. Using the individual syrups 11 mixtures were made of which

three resembled the composition of the multifruit juices. Mixtures 1, 2 and

3 respectively correspond to Fruitdrink ACE, Benefits Vitality and Benefits

Immunity. The composition of all 11 mixtures is listed in Table 3.4. The

nine individual syrups, the three multifruit juices and the 11 mixtures were

analyzed in triplicate using the ETSPU and ATR-FTIR (Chapter 4).

3.2.2 Electronic tongues

3.2.2.1 Electronic tongue Saint-Petersburg University

The ETSPU consists of a sensor array of 18 to 27 potentiometric chem-

ical sensors. Non-specific sensors with both chalcogenide glass and PVC

plasticized membranes are comprised into the sensor arrays. The array con-

tains anionic sensors and cationic sensors, selected for their sensitivity to

organic acids and minerals, and a pH sensor. No information can be given

about the sensor composition due to a secrecy policy at the University of

Saint-Petersburg. Sensor potential values are measured versus a conven-

tional Ag/AgCl reference electrode with a precision of 0.1 mV. When the

sensors are immersed in a sample, within three minutes a potential over the

electrode membrane reaches an equilibrium value which is related to the

chemical composition of the sample. The potential values are recorded in


Table 3.3: Composition of three multifruit juices: Fruitdrink ACE, Benefits Vi-

tality and Benefits Immunity.

Fruitdrink ACE Benefits Vitality Benefits Immunity

Fruit

Blend-9 fruit 30% 11% 0%

Lemon 1% 0% 0%

Orange 0% 32% 0%

Passion fruit 0% 2.5% 0%

Apple 0% 53% 62.5%

Red grape 0% 0% 16%

Elderberry 0% 0% 9.5%

Cherry 0% 0% 2.5%

Strawberry 0% 0% 3.5%

Total fruit 31% 94% 94%

Extra compounds

Water yes no no

Aloe vera puree no yes yes

Provitam A yes no no

Fibers no yes yes

Vitamin C yes no no

Vitamin E yes yes yes

Minerals no yes yes

Aromas yes yes no

Sweetener yes no no


Table

3.4

:C

omp

osit

ion

of

11

mix

ture

sm

ad

eou

tof

ind

ivid

ual

syru

ps

(%v/v).

Ble

nd-9

Lem

onO

rang

eP

assi

onA

pple

Red

Eld

erbe

rry

Che

rry

Stra

wbe

rry

frui

tfr

uit

grap

e

197

3

210

332.

554

365

1710

2.6

4

490

10

528

8.5

286

28

650

2010

20

766

33

866

1616

958

2227

83

1040

204

410

119

4523

23


data files. The ETSPU measurements were performed on apple and

tomato samples which were juiced using a mixer. Several measurements

were performed to determine the ideal sample preparation for ETSPU anal-

ysis. Using a fruit juice:water ratio of 1:5 makes it possible to detect a clear

sensor response and minimizes the amount of sample needed per assay. Ten

mL of juice was diluted with 50 mL of distilled water to reach a total vol-

ume which allowed all sensors to be immersed in the sample. In between the

measurements the sensors were rinsed using distilled water for seven min-

utes until stable sensor readings were recorded and the baseline potential

was reached again. After one and four minutes the distilled water was re-

newed. Two different sets of different sensors were used in the experiments

described in this thesis. A first set comprising 18 sensors was used in the

experiment dealing with both apple and tomato cultivars. A second set of

27 potentiometric sensors was applied in the experiment comparing two ET

systems and the experiment dealing with multifruit juices.

3.2.2.2 ASTREE electronic tongue Alpha M.O.S.

The ASTREE ET developed by Alpha M.O.S. (Toulouse, France) is com-

posed of seven liquid sensors. The commercially available set #1 (sensors ZZ,

BA, BB, CA, GA, HA and JB) was chosen for this particular experiment.

The sensors show selectivity to sugars, acids and minerals (AlphaM.O.S.,

2001a,b, 2006). Despite the fact that Alpha M.O.S. says that the sensors

give stable readings after one minute, the measurement time was set equal

to that of the ETSPU, i.e. three minutes. Following the guidelines of Alpha

M.O.S., the measurements were performed using 90 mL centrifuged tomato

juice. A total volume of 150 mL tomato juice was required to reach 90

mL centrifuged juice. Samples were centrifuged using a centrifuge (KR 22i

Jouan, Saint-Herblain Cedex, France) at 25000 g during five minutes. The

sample volume was not minimzed by dilution as with the ETSPU. In be-

tween measurements the sensors were rinsed using distilled water during 20

seconds, as Alpha M.O.S. prescribes. The sensors did not always reach their

baseline potential after this short cleaning period causing large drift in the


measurement data. Despite this, data analysis was performed on absolute

data, as advised by the Alpha M.O.S. company, instead of on relative data.

3.2.3 Reference techniques

3.2.3.1 HPLC

The frozen apple and tomato samples were ground into a fine powder. The

grinding was partly performed by hand, using a mortar, and partly me-

chanically with a homogenizer (MM 200, Retsch, Haan, Germany). 0.1 g

of the frozen powder was transferred into a cooled 1.5 mL eppendorf tube

(Eppendorf, Hamburg, Germany) and again stored at −80 ◦C until the ex-

traction of the samples was performed. The organic acids and sugars were

extracted by adding 500 µL of 80% v/v ethanol/water to the frozen sam-

ples. After incubation in a temperature controlled shaker (Thermomixer

Comfort, Eppendorf, Hamburg, Germany) at 78 ◦C during 10 minutes, and

centrifugation at 4 ◦C and 20000 g during 5 minutes (Hawk 15/05 Refriger-

ated centrifuge, Sanyo, Bensenville, USA) the supernatant was transferred

in two new eppendorf tubes. For the analysis of organic acids 300 µL of

supernatant was used, for sugars 100 µL. Subsequently the eppendorfs with

the pellet were dry centrifuged (Concentrator 5301, Eppendorf, Hamburg,

Germany). For organic acid and sugar analysis, respectively 100 µL and 200

µL of HPLC water (Fisher Scientific, Loughborough, UK) was added to the

samples. After incubation, at the same conditions as mentioned before, the

samples were filtered on a 0.45 µm pore space filter (Alltech Associates Inc.,

Deerfield, USA).

The analysis of the organic acids and sugars were carried out on a Series

1100 HPLC (Agilent Technologies Inc., Palo Alto, USA). The acids were sep-

arated on a Prevail Organic Acid column (Alltech Associates Inc., Deerfield,

USA) at room temperature with a mobile phase of formic acid (pH = 2.5).

The organic acids were detected with a diode array detector (DAD) at 200

nm. The sugars were separated on an Aminex column (Bio-Rad, Hercules,

USA) with water as mobile phase and at a column temperature of 80 ◦C. The


sugars were detected with a refractive index detector (RID). Chemstation

software version 10.01 (Agilent technologies Inc., Palo Alto, USA) was used

to operate both HPLC systems and collect the chromatograms. Calibration

curves for malic acid, citric acid, sucrose, glucose and fructose were made.

Hereto individual chemical components were purchased at Sigma Aldrich

(Steinheim, Germany).

3.2.3.2 Enzymatic high throughput technique

An enzymatic high throughput method (EHT) was used as a reference

technique to evaluate sugar and acid content of the tomato samples in

the experiment comparing two ET’s. An automated liquid handling sys-

tem (Multiprobe II Plus, Perkin Elmer, Boston, USA) with four channels

was programmed to dispense all the reagents in the wells of the microtitre

plates. 96-well (NUNC, Roskilde, Denmark) and 384-well (Corning, New

York, USA) flat-bottomed non-treated polystyrene microtitre plates were

used. The absorbances at the specified wavelengths were read with a Multi-

skan Spectrum (Thermo Electron Corporation, Waltham, USA). The enzy-

matic assays for the analysis of glucose, fructose, citric acid, malic acid and

glutamate were purchased from R-Biopharm (Darmstadt, Germany). The

assays are based on an increase/decrease in absorbance at specific wave-

lengths caused by a change in NAD(P)H (340 nm). The absorbance of the

chromogenic molecules is measured before and after the addition of the sub-

strate specific enzyme and is corrected for the delta absorbance of the blank

values. The tomato samples were filtered using a 0.45 µm pore filter (All-

tech Associates Inc., Deerfield, USA) preceding the analysis. All samples

were analyzed in duplicate together with a calibration curve, consisting of

four points with three repetitions per concentration, on the same microtitre

plate. All compounds were purchased at Sigma Aldrich (Steinheim, Ger-

many). Since the concentrations of the acids and sugars in the samples were

too high to be analyzed directly, dilution with distilled water was necessary

to obtain concentrations that were in the linear range of the calibration

curve. A detailed description is given by Vermeir et al. (2007). Four repe-


titions of each cultivar were analyzed using this fast reference technique.

3.2.3.3 Atomic absorption spectroscopy

For the analysis of minerals in the tomato samples, atomic absorption spec-

troscopy (AAS) was applied as a reference technique. The concentrations of

Na and K, which have an influence on the saltiness of the tomato samples,

were determined using a flame atomic absorption spectrometer (Solaar 969

A Thermo Elemental, Cambridge, U.K.). The tomato samples were filtered

using a 0.45 µm pore filter.

3.2.4 Statistical analysis

The multidimensional signals of the electronic tongues required data pre-

treatment before statistical analysis could be performed. Both the ETSPU

and the ASTREE ET comprised potentiometric sensors of which some were

sensitive to drift during the experiment. The drifting sensors were deleted

from the sensor array based on the sensor stability. A coefficient of varia-

tion (CV value) was calculated for each sensor and cultivar. The CV value

is defined as the standard deviation divided by the mean, multiplied by 100

percent. Sensors with an average CV value of more than 10 were considered

as unstable during the experiment.

Multivariate data analysis was applied for both qualitative and quan-

titative analysis using both multisensor systems. Partial least squares-

discriminant analysis (PLS-DA) was used for data visualization and clus-

tering of observations in the data structure. Using this technique, intra-

cultivar effects are minimized and inter-cultivar effects are maximized. The

analysis was performed on the covariance matrix. Outliers were deleted from

the analysis based on their scores, leverages (distance to the model centre

for each object summarized over all components) and residuals (Geladi and

Dabakk, 1995). The results of the PLS-DA performed on the ET data were

compared to those of the reference techniques.


Principal component analysis (PCA), an unsupervised method, was used

as a data exploration technique on the data of the fruit juices, syrups and

mixtures. The potential of the ETSPU as a tool for quality control was

examined using this technique. The multifruit juices, syrups and their mix-

tures were grouped using PCA (Johnson and Wichern, 1992).

Partial least squares analysis (PLS) was performed to study the pre-

dictive capacity of the ET’s for individual compounds and syrups. The

calibration models were validated using cross-validation, where a small set

of randomly selected samples is left out of to construct the model and is

used afterwards for validation purposes. PLS2 was used for the prediction

of the sugars, acids and minerals in apples and tomatoes and the prediction

of syrups in multifruit juices. In a PLS2 analysis, all compounds of interest

(Y variables) are related together to the sensor readings (X variables). The

results of the HPLC or EHT and AAS measurements were taken as ref-

erences for the assessment of individual chemical compounds in apple and

tomato. For the prediction of individual compounds in the artificial juice,

PLS1 was used. In this case, the concentration of the glucose, citric acid

and malic acid are related one by one (one Y variable) to the sensor read-

ings (X variables) since each time only one compound was varied in the

artificial juices (Martens and Naes, 1998). Prediction models having a cor-

relation above 0.90, high slope and low offset were considered to be good.

The correlation gives information about the quality of the model, but gives

no direct information about the prediction accuracy. The RPD value is a

factor which indicates the accuracy. An RPD value between 2 and 2.5 makes

approximate quantitative predictions possible. For values between 2.5 and

3, and above 3, the prediction is classified as good and excellent, respectively

(Saeys et al., 2005). For data analysis two different computer software pro-

grams were used: The Unscrambler version 9.1.2 (CAMO Technologies Inc.,

Oslo, Norway) and SAS version 9.1 (SAS Institute Inc., Cary, USA).

58 3.3 Results

3.3 Results


tification of taste compounds

3.3.1.1 Classification with HPLC

Differences in the concentrations of all individual compounds between the

cultivars are studied looking at the average values. The results of the HPLC

data of both the apple and tomato samples are shown in Table 3.5. The

apple samples contain high concentrations of fructose. While Pinova has the

highest concentration of this sugar, this cultivar has a low concentration of

sucrose. Fructose, sucrose and malic acid are the most abundant chemical

taste compounds in apple. The differences in the content of sucrose and

malic acid between the different cultivars are large. Cox and Golden contain

a higher concentration of sucrose than the other three cultivars. The apple

cultivar Cox shows a higher content of sucrose and malic acid than most of

the other cultivars.

Tomatoes, on the other hand, contain high concentrations of citric acid.

The highest concentration of this acid is found with Climaks and Clotilde.

Glucose and fructose are the two most abundant sugars in the tomato sam-

ples. Aranca contains higher glucose and fructose contents and lower citric

acid and malic acid concentrations than the other three cultivars. This

tomato cultivar is a cherry tomato which is known for its sweet taste.

After a first screening of the data, the results of the reference measure-

ments of all compounds were jointly analyzed using multivariate data anal-

ysis. The possibility of HPLC to group samples of one cultivar and, thus,

to classify cultivars based on their content of taste compounds is studied

using PLS-DA. Figures 3.1 and 3.2 show the PLS-DA results of respectively

the apple and tomato measurements performed by HPLC as reference tech-

nique. Elstar is positioned in the center of the score plot, while the other

four cultivars each are placed in one of the quadrants. The within cultivar

variance is caused by biological variability and measurement error. Along


Table 3.5: Average results of HPLC measurements performed on apple and tomato

samples (average ± standard deviation, concentrations in mg/g powder).

Cultivar Sucrose Glucose Fructose Malic acid Citric acid

Apple

Cox 26±3 3.8±0.6 24±2 12±2 0.26±0.05

Elstar 12±2 7±1 21±3 10±2 0.21±0.03

Golden 21±3 6±1 26±5 12±2 0.16±0.04

Jonagold 12±2 8±2 28±4 7±1 0.15±0.04

Pinova 11±2 8.9±1 29±4 8±1 0.21±0.04

Tomato

Aranca 8.4±0.7 22±2 20±2 0.10±0.04 11±1

Climaks 6±1 12±3 13±3 1.4±0.3 18±4

Clotilde 4±2 17±2 15±1 0.9±0.1 18±3

DRW 73-29 4.1±0.7 13±2 12±1 1.3±0.2 15±2

the axis of the first principal component (PC) apple cultivar Cox is clearly

separated from the other cultivars. The trend which is visible along the axis

of the first PC is related to the malic acid, sucrose and glucose content of

the samples. The order in which the apple cultivars appear along this axis,

is the same trend that is found in the malic acid content (Table 3.5) going

from Cox over Golden and Elstar to Jonagold and Pinova. In the correla-

tion loadings plot two ellipses are visible. The inner and outer ellipses on

the figures represent correlation coefficients (R) of 70% and 100% (or R2

values of 50% and 100%). For a taste compound located between the two

ellipses more than 70% of its variability is explained by the first two prin-

cipal components. This means this variable is important in describing the

variability, and, hence, the major cultivar effects, within the data set. The

correlation loadings plot shows that Cox is highly related to both malic acid

and sucrose. Golden and Jonagold can be separated, though not completely,

from the other cultivars in the direction of the second PC. The trend along

this axis is mainly caused by citric acid. The HPLC results show indeed

that Pinova, Elstar and Cox have a higher content of these compounds than

Golden and Jonagold. Projected in the two dimensional space of the first

two PC’s, Jonagold is correlated to fructose.

60 3.3 Results

-2

-1

0

1

2

3

-4 -3 -2 -1 0 1 2 3 4

PC

2

PC 1

CoxElstarGoldenJonagoldPinova

A

Malic acid

Citric acid

GlucoseCox

ElstarPinova

0 0

0.2

0.4

0.6

0.8

1.0

PC

2

PC 1Malic acid

SucroseFructose

Golden

Jonagold

-1.0

-0.8

-0.6

-0.4

-0.2

0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

B

Figure 3.1: Score plot (A) and correlation loadings plot (B) of the PLS-DA of the

apple samples measured by HPLC (X-expl. (52%, 16%); Y-expl. (21%, 15%)).


-2

-1

0

1

2

-4 -3 -2 -1 0 1 2 3

PC

2

PC 1

ArancaClimaksClotildeDRW 73-29

A

Malic acid

Citric acid

Sucrose

Glucose

Fructose

Aranca

Climaks

Clotilde

DRW 73-29

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

PC

2

PC 1

B


tomato samples measured by HPLC (X-expl. (74%, 10%); Y-expl. (31%, 21%)).

62 3.3 Results

The tomato results (Figure 3.2) show a separation between the cultivars

based on the HPLC results. The within cultivar variance, here too, is mainly

caused by biological variability. Aranca is clearly separated from the other

tomato cultivars. This separation is caused based on the glucose, fructose

and sucrose content of the cherry tomato. In the correlation loadings plot

Aranca is highly correlated with these three sugars. The concentrations

found in Aranca are different from the other cultivars (Table 3.5). In the

direction of the second PC Climaks can be classified separated from Clotilde

and DRW 73-29. This cultivar, however, can not be correlated highly to one

of the chemical compounds which were analyzed.

3.3.1.2 Classification with ETSPU

The ETSPU used in this experiment is comprised of 27 potentiometric sen-

sors of which some were sensitive to drift during the experiment with apples

and tomatoes. The drifting sensors were deleted from the sensor array in

each experiment separately based on the sensor stability. The CV values of

all sensors of the ETSPU system are shown in Table 3.6 for apple and Table

3.7 for tomato. Sensors with a CV value of more than 10 were considered

as unstable during the experiment. In the experiment with the apples, five

sensors were deleted from the data matrix. In the tomato experiment, 21

sensors were retained for further data analysis. Four of the discarded sensors

are the same for both apple and tomato. At this moment, however, should

be stated that the sensors were not deleted from the data matrix based only

on their instability. Since one fruit was taken as one sample, biological vari-

ability is also included in the experiment. This biological variability could

also have caused a variation in the data which is recognized as sensor in-

stability. Since no changes could be made to the experiment, the possible

effect of biological variability was ignored.

The ETSPU data from apple and tomato were jointly analyzed with PLS-

DA. Apple and tomato are clearly separated from each other in the score

plot (Figure 3.3). All five apple cultivars are positioned at the negative end

of the first PC, while the four tomato cultivars are located at the positive


Table 3.6: CV values of the sensors of the ETSPU (apples).

Sensor CV value Sensor CV value

1 2.5 16 112

2 7.4 17 3.7

4 4.0 18 5.7

5 24 19 4.7

6 3.0 20 11

7 2.8 21 1.5

8 2.2 22 8.4

9 2.9 23 7.6

10 7.3 24 6.3

11 6.2 25 24

12 1.7 26 12

13 4.3 27 7.0

14 3.8 28 2.3

15 4.2

Table 3.7: CV values of the sensors of the ETSPU (tomatoes).


1 3.3 16 100

2 4.4 17 5.5

4 4.1 18 8.0

5 54 19 5.4

6 7.0 20 11

7 5.8 21 1.8

8 6.0 22 7.9

9 3.4 23 7.0

10 5.4 24 13.8

11 5.1 25 2.4

12 1.4 26 20

13 3.5 27 19

14 3.3 28 2.4

15 6.8

64 3.3 Results

-80

-60

-40

-20

0

20

40

60

-200 -150 -100 -50 0 50 100 150 200

PC

2

PC 1

Apple Tomato

Figure 3.3: Score plot of the PLS-DA of the apple and tomato samples measured

using the ETSPU (X-expl. (72%, 16%); Y-expl. (82%, 12%)).

end. The correlation loadings plot (not shown) indicates that apple is highly

correlated with malic acid, sucrose and fructose and sensors 1, 4, 6, 7, 8,

9, 12, 19, 21, 25 and 28. Tomato is correlated with citric acid, glucose and

sensors 14 and 15. Table 3.5 lists that apple indeed contains more malic

acid, sucrose and fructose than tomato, which contains more citric acid and

glucose. The correlation loadings plot indicates a relation between malic

acid, sucrose and fructose and 11 sensors of the ETSPU. Citric acid and

glucose are moderately related with sensors 14 and 15.

The results of the PLS-DA performed on the ETSPU readings of the ap-

ple samples are shown in Figure 3.4. Pinova is positioned slightly separated

from the other apple cultivars. Based on the correlation loadings of this

analysis (not shown), however, no sensors are appointed as being responsi-

ble for this classification. Also, no explanation is found based on the sugar


and acid content as measured by HPLC. The reason for the separation of

Pinova from the other four apple cultivars might be caused by differences

in mineral content. Minerals were, however, not analyzed in this experi-

ment. The spreading of the samples within one cultivar is larger using the

ETSPU, compared to the reference analysis (Figure 3.1). The classification

results of both the reference technique and ET are presented in Table 3.8. A

large difference is present between HPLC and ETSPU. Using the reference

technique, more samples are classified correctly.

-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6 8

PC

2

PC 1


Figure 3.4: Score plot of the PLS-DA of the apple samples measured using the

ETSPU (X-expl. (42%, 20%); Y-expl. (17%, 10%)).

Figure 3.5 shows the results of the same analysis performed on the

tomato data. Here too, the within cultivar variance in larger than in the

analysis performed on the HPLC data (Figure 3.2), especially for Aranca.

Aranca is separated from the other tomato cultivars. This tomato cultivar

is, as indicated by the correlation loadings plot (not shown), correlated to

66 3.3 Results

sensors 1, 12, 19 and 21 of the ETSPU. This indicates that these sensors are

related to the three sugars present in the samples, which could also be seen

in previous analysis of apples and tomatoes together (Figure 3.3), where

these sensors are related to sucrose and fructose. The other three cultivars

are not correlated to any of the sensors in the space created by the first two

PC’s. Table 3.8 shows that, as with the apples, the percentage of correct

classified samples is less with the ETSPU than with HPLC.

2

4

6

PC

2

PC 1

ArancaClimaksClotildeDRW 73-29

-6

-4

-2

0-8 -6 -4 -2 0 2 4 6 8 10

Figure 3.5: Score plot of the PLS-DA of the tomato samples measured using the

ETSPU (X-expl. (43%, 13%); Y-expl. (15%, 10%)).


Table 3.8: Classification results of the PLS-DA performed on the reference data

and ETSPU measurements on apple and tomato. The percentage of correct classi-

fied samples is shown (%).

Cultivar HPLC ETSPU

Cox 100 25

Elstar 75 75

Jonagold 85 0

Golden 35 25

Pinova 60 90

Aranca 100 85

Climaks 90 60

Clotilde 65 15

DRW 73-29 85 10

3.3.1.3 Quantification with ETSPU

Next to classification, the potential of the ETSPU to quantify chemical

components was studied in a PLS analysis. Since the ETSPU gives potential

readings, the logarithm of the concentration of all compounds is used for the

analysis. Using the model solutions, resembling artificial fruit juices, PLS

models were built for each of the compounds of interest. Since different

mixtures were made for each compound, PLS1 regression was used. The

results of the prediction models for glucose, malic acid and citric acid are

shown in Table 3.9. The calibration models for citric acid and malic acid

have high correlations between the measured and predicted concentrations.

The correlations between the measured and predicted concentration in

the validation models of both acids are very high too, with values of respec-

tively 99% and 98% for malic acid and citric acid. In addition, both the

RMSEC and RMSECV values are low. Despite the good results for citric

acid, a large difference is present between the slopes of the calibration and

validation model. With a slope of 85%, the predicted values of citric acid

are an underestimation of the observed values. The models for glucose are

not satisfactory. The validation model is very different from the calibration

68 3.3 Results

model with a lower correlation and a higher RMSECV value. The corre-

lation between the measured and predicted concentration of glucose in the

validation model is only 83%. The high RMSE values indicate that the cal-

ibration model of glucose is better than the validation model. The ETSPU,

however, has an excellent accuracy to predict the concentrations of citric

acid and malic acid in a chemical solution, with high correlations and RPD

values of respectively 4.4 and 7.2. Since the ETSPU is a potentiometric

device, ions are measured. This explains why the prediction of citric acid

and malic acid is better than that of glucose.

Table 3.9: PLS1 models based on the ETSPU readings of artificial juices (log-

arithm of concentration). Cross-validation was used to validate the model. The

offsets, RMSEC and RMSECV values are given in g/L.

Compound Slope Offset Correlation RMSEC RPD

RMSECV

Glucose Calibration 0.95 0.00 0.98 0.04

Validation 0.97 0.09 0.83 0.16 1.5

Malic acid Calibration 0.99 0.00 0.99 0.04

Validation 0.94 0.01 0.99 0.15 7.2

Citric acid Calibration 0.98 0.02 0.99 0.05

Validation 0.85 0.18 0.98 0.08 4.4

A PLS2 regression was performed to predict the concentration of the

taste compounds of apple and tomato. The results of the analysis for apple

and tomato are shown in respectively Table 3.10 and 3.11. The calibration

and validation models of both apple and tomato samples do not show any

possibility of the ETSPU to predict the chemical composition of the sam-

ples. All correlations are too low to talk about a good correlation between

instrumental techniques. Both for apple and tomato, the ’best’ PLS model

is made for malic acid. The correlations of the calibration models are re-

spectively 80% and 89% for apple and tomato. The offsets of the models

made for malic acid are close to zero. The slopes, however, are low and the

RMSEC values, which are a measure for the prediction error of the model,

are high. The validation models of malic acid are also less good with cor-


relations of 74% for apple and 84% for tomato and high RMSECV values.

The RPD values are all below 2, indicating that the made models can not

be used to predict the chemical composition of the samples.

Table 3.10: PLS2 models based on the ETSPU readings of apples (logarithm

of concentration) Cross-validation was used to validate the model. The offsets,

RMSEC and RMSECV values are given in mg/g.


RMSECV

Malic acid Calibration 0.64 -0.11 0.80 0.15

Validation 0.61 -0.12 0.74 0.17 0.7

Citric acid Calibration 0.28 -2.25 0.53 0.23

Validation 0.20 -2.49 0.37 0.25 0.5

Sucrose Calibration 0.66 0.40 0.81 0.10

Validation 0.61 0.47 0.75 0.11 1.6


Validation 0.49 0.41 0.66 0.12 1.3

Fructose Calibration 0.41 0.82 0.64 0.06

Validation 0.35 0.91 0.55 0.07 1.2

Table 3.11: PLS2 models based on the ETSPU readings of tomatoes (logarithm

of concentration). Cross-validation was used to validate the model. The offsets,

RMSEC and RMSECV values are given in mg/g


RMSECV


Validation 0.74 -0.05 0.84 0.26 1.9


Validation 0.46 0.64 0.62 0.09 1.4


Validation 0.49 0.38 0.66 0.12 1.4


Validation 0.41 0.69 0.59 0.09 1.6


Validation 0.35 0.76 0.54 0.08 1.5

70 3.3 Results

3.3.2 Comparison of two electronic tongues

3.3.2.1 Classification with reference techniques EHT and AAS

The average concentrations of carbohydrates, organic acids and minerals

measured by the reference techniques, EHT and AAS, are shown in Table

3.12. Amoroso, a cherry cluster tomato, has high concentrations of both

sugars, citric acid, glutamate and both measured minerals and a low con-

centration of malic acid. Due to its chemical content this is a very tasty

tomato with a specific sweet and sour taste. Sunstream, another cocktail

cluster tomato, also shows high concentrations of both sugars, but lower than

Amoroso. Tricia has low concentrations of all compounds. This cultivar is

known for its unpronounced taste (Buysens, 2006a). Admiro, Macarena

and Clotilde all have intermediate concentrations of sugars and acids, they,

however, have different tastes (Table 3.2).

In the PLS-DA performed on the data of both reference techniques, EHT

and AAS, the six tomato cultivars are clearly separated from each other

(Figure 3.6). Amoroso, the cherry cluster tomato, is clearly separated from

the other cultivars. The axis of the first PC can be seen as a sugar axis, going

from Amoroso with a high sugar content to Tricia with low concentrations

of all sugars. Admiro and Tricia cannot be separated from each other along

the axis of the first PC, which is related to the sugar and glutamate content.

Table 3.12 shows that these two cultivars do not differ a lot in their fructose

and glutamate content. The concentrations of citric and malic acid however

are different in both tomato cultivars. Thus, the separation along the axis of

the second PC is caused by this difference in acid concentration. Negative

scores for PC2 correspond to high concentrations of citric acid and malic

acid. Despite the fact that Admiro and Macarena have a very different

chemical composition, they are close to each other in the score plot of the

PLS-DA.


-2

-1

0

1

2

-14 -12 -10 -8 -6 -4 -2 0 2 4 6 8

PC

2

PC 1

AdmiroMacarenaSunstreamAmorosoTriciaClotilde

A

Glucose

Citric acid

Malic acid

Admiro

Macarena

Sunstream

0 0

0.2

0.4

0.6

0.8

1.0

PC

2

PC 1Fructose

GlutamateAmoroso

Tricia

Clotilde

-1.0

-0.8

-0.6

-0.4

-0.2

0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0


tomato samples measured by EHT and AAS (X-expl. (74%, 16%); Y-expl. (19%,

19%)).

72 3.3 Results

Table

3.1

2:

Ave

rage

resu

lts

of

EH

Tan

dA

AS

mea

sure

men

tsp

erfo

rmed

on

tom

ato

sam

ple

s(a

vera

ge±

stan

dard

dev

iati

on

,co

nce

ntr

ati

on

sin

g/L

juic

e).

Cul

tiva

rG

luco

seFr

ucto

seC

itri

cM

alic

Glu

tam

ate

Na

K

acid

acid

Adm

iro

11.2±

0.3

11.3±

0.3

4.8±

0.5

0.72±

0.01

0.95±

0.03

9.2±

0.2

1.8±

0.2

Mac

aren

a15

.6±

0.2

14.4±

0.2

4.3±

0.5

0.88±

0.02

0.64±

0.02

7.4±

0.4

1.9±

0.3

Suns

trea

m17

.7±

0.4

17.4±

0.3

5.4±

0.4

0.51±

0.01

1.45±

0.02

7.9±

0.6

2.2±

0.3

Am

oros

o23

.7±

0.3

23.5±

0.5

5.8±

0.3

0.27±

0.01

2.82±

0.08

82.9±

4.1

2.7±

0.1

Tri

cia

10.0±

0.1

10.8±

0.2

2.9±

0.4

0.51±

0.01

0.88±

0.03

8.8±

0.6

1.6±

0.1

Clo

tild

e13

.3±

0.2

13.4±

0.2

3.7±

0.3

0.55±

0.01

1.07±

0.01

46.0±

3.4

1.77±

0.04


3.3.2.2 Classification with ETSPU and ASTREE ET

The multidimensional signals of both ET’s required data pretreatment be-

fore statistical analysis could be performed. Drifting sensors were deleted

from both sensor arrays based on the sensor stability. The CV values of

all sensors of both ET systems are shown in Table 3.13 and 3.14. Since

no biological variability was present in this experiment, it can be concluded

that high CV values represent sensor instability. Sensors with a CV value

of more than 10 were considered as unstable during the experiment. Four-

teen sensors out of 18 of the ETSPU were retained for further data analysis.

The extreme CV values found for sensor 12 will probably be due to arte-

facts. One sensor, sensor JB, was deleted from the ASTREE sensor array

for analysis.

Table 3.13: CV values of the sensors of the ETSPU.


1 2.2 10 3.5

2 3.5 11 7.2

3 3.8 12 2931

4 4.3 13 13

5 2.5 14 12

6 1.4 15 2.0

7 1.5 16 1.3

8 2.7 17 2.2

9 3.7 18 774

Figure 3.7 shows the results of the PLS-DA on the data of the ET-

SPU. Along the axis of the first PC three groups of cultivars are visible.

Amoroso on the negative side of this PC is separated from the other cul-

tivars. Macarena and Tricia are positioned at the positive end of the axis,

while Admiro, Sunstream and Clotilde are located in the center of the score

plot. This clustering is probably caused by the difference in the content of

both sugars and minerals as measured by the reference techniques (Table

3.12). In the correlation loadings plot, Amoroso is correlated with four sen-

74 3.3 Results

Table 3.14: CV values of the sensors of the ASTREE ET developed by Alpha

M.O.S.

Sensor CV value

ZZ 3.6

BA 4.0

BB 0.6

CA 1.8

GA 4.5

HA 3.8

JB 38

sors: sensors 8, 9, 10 and 11. Because of the high sugar and mineral content

of this cultivar, these sensors might be related to the sugars and minerals

present in the samples. Macarena and Tricia are somewhat correlated with

respectively sensors 4 and 15 and sensors 3 and 7. The percentage of correct

classified samples of the ETSPU is shown in Table 3.15. Comparing the re-

sults of the reference techniques to those of the ETSPU, it can be concluded

that the multisensor system is not able to classify the samples as good as

the reference techniques. None of the samples of Macarena and Sunstream

are classified correctly.

Table 3.15: Classification results of the PLS-DA performed on the reference data,

ETSPU and ASTREE ET measurements. The percentage of correct classified sam-

ples is shown (%).

Cultivar EHT/AAS ETSPU ASTREE ET

Admiro 80 67 100

Macarena 100 0 100

Sunstream 100 0 90

Amoroso 100 67 100

Tricia 100 25 90

Clotilde 100 17 90


-4

-2

0

2

4

-8 -6 -4 -2 0 2 4 6

PC

2

PC 1


A

S1

S2

S3

S5

S7S9

S17

Admiro

Sunstream

0 0

0.2

0.4

0.6

0.8

1.0

PC

2

PC 1

S4

S6

S8S9

S10S11

S15

S16

Macarena

Amoroso TriciaClotilde

-1.0

-0.8

-0.6

-0.4

-0.2

0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

B


tomato samples measured using the ETSPU (X-expl. (58%, 22%); Y-expl. (18%,

8%)).

76 3.3 Results

-4

-3

-2

-1

0

1

2

3

-4 -3 -2 -1 0 1 2 3

PC

2

PC 1


A

Sensor ZZSensor BA

Sensor BB

Sensor CA

Sensor GA

Sensor HA

MacarenaTricia

Clotilde

0 0

0.2

0.4

0.6

0.8

1.0

PC

2

PC 1

Admiro

SunstreamAmoroso

-1.0

-0.8

-0.6

-0.4

-0.2

0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

B

Figure 3.8: Score plot (A) and correlation loadings plot (B) of the PLS-DA of

the tomato samples measured using the ASTREE ET developed by Alpha M.O.S.

(X-expl. (45%, 33%); Y-expl. (14%, 7%)).


The results of the PLS-DA performed on the data resulting from the

ASTREE ET, after exclusion of sensor JB from the dataset, are shown in

Figure 3.8. Two groups are found along the axis of the first PC. Amoroso

and Clotilde are separated from the other four cultivars. The clustering of

these two cultivars, however, cannot be explained by their chemical composi-

tion, since both cultivars have very different concentrations and proportions

of sugars, acids and minerals (Table 3.12). In the direction of the second

PC Admiro is classified separated from the other cultivars. No important

correlations between the tomato cultivars and the sensors are noticed in the

correlation loadings plot. The ASTREE ET clearly contains different in-

formation than the reference techniques. The multisensor system, however,

is able to classify most of the samples within the correct cultivar (Table

3.15). Compared to the results of the ETSPU, the ASTREE ET is better

in classifying the samples by cultivar.

3.3.2.3 Quantification with ETSPU and ASTREE ET

After studying the ability of both ET’s to classify tomato cultivars, the

potential of both multisensor systems to quantify the chemical content of the

samples was examined. In PLS2 models the sensor readings were coupled to

the results of the reference measurements. The results of the PLS2 analyses

are shown in Table 3.16 and Table 3.17. Since the ETSPU gives potential

readings, the logarithm of the concentration of all compounds is used for

the analysis. As shown in Table 3.16, the ETSPU is able to predict the

concentration of all sugars, acids and minerals of interest. The PLS2 models

show slopes close to one and low offsets. All slopes are higher than 90%,

except for the validation model of citric acid, and all offsets are close to zero.

The correlations between measured and predicted values of all PLS2 models

are high and close to one. The RMSECV values, which are a measure

for the prediction error, are very low. The fact that the calibration and

validation models are very similar to each other is another proof of the

reliability of these models. The high RPD values indicate that the ETSPU

is highly suited to determine glucose, fructose, malic acid, glutamate and

78 3.3 Results

Na. The RPD values of the models predicting citric acid and K are between

2 and 2.50, indicating that approximate predictions of these compounds are

possible using the ETSPU.

Table 3.16: PLS2 models based on the ETSPU readings of tomatoes (logarithm

of concentration). Cross-validation was used to validate the model. The offsets,

RMSEC and RMSECV values are given in g/L.


RMSECV


Validation 0.93 0.08 0.95 0.04 3.2


Validation 0.95 0.05 0.96 0.03 3.9


Validation 0.84 0.10 0.87 0.05 2.3


Validation 0.94 -0.02 0.96 0.05 3.2

Glutamate Calibration 0.96 0.003 0.98 0.04

Validation 0.94 0.002 0.95 0.06 3.5

Na Calibration 0.99 0.01 0.99 0.04

Validation 0.92 0.10 0.97 0.10 4.1

K Calibration 0.95 0.16 0.97 0.02

Validation 0.91 0.30 0.91 0.04 2.3

The PLS2 models based on the measurements of the ASTREE ET are

listed in Table 3.17. The results are very different from those of the ETSPU.

In case of the ASTREE ET the concentrations of the individual compounds

were used in the model as prescribed by the Alpha M.O.S. company. A log-

arithmic transformation was tried in the analysis, but the most satisfactory

results were found using raw data. All compounds show PLS2 prediction

models which are not satisfactory. The slopes and offsets of both calibration

and validation models are not acceptable. All slopes are low and the offsets

of glucose, fructose and, especially, K are far from zero. The correlations

between measured and predicted values of both the calibration and valida-

tion models of glutamate and Na are acceptable, ranging between 80% and


94%. The models built for the other sugars, acids and K show correlations

that are not sufficient to ensure good predictions. The RMSECV values of

all models are in line with the slope, offset and correlation, showing high

values for glucose, fructose and K, but also for Na. All RPD values are very

low, indicating that the models are not good. From these results can be

stated that the ASTREE ET equipped with this set of sensors is not able

to quantify individual chemical compounds in tomato juices.

Table 3.17: PLS2 models based on the ASTREE ET readings of tomatoes. Cross-

validation was used to validate the model. The offsets, RMSEC and RMSECV

values are given in g/L.


RMSECV


Validation 0.36 9.73 0.49 3.90 0.03


Validation 0.47 8.05 0.56 3.52 0.03


Validation 0.54 2.15 0.62 0.89 0.1


Validation 0.58 0.25 0.72 0.12 1.4


Validation 0.74 0.33 0.80 0.41 0.5

Na Calibration 0.87 3.12 0.94 9.53

Validation 0.80 5.56 0.85 15 0.03

K Calibration 0.54 910 0.74 270

Validation 0.37 1255 0.52 348 0.0003

3.3.3 Quality control of fruit juices

The ETSPU with a sensor array of 18 potentiometric sensors was used for

the measurement of fruit juice compositions. The same set of sensors was

used as in the previous experiment. None of the sensors were sensitive to

drift during the experiment. The CV values of all sensors of the ETSPU

system are shown in Table 3.18. After the previous experiment dealing with

80 3.3 Results

tomato samples, sensor 12 was replaced by a new sensor. All sensors were

included in the data analysis.

Table 3.18: CV values of ET sensors.


1 0.81 10 1.1

2 1.4 11 2.4

3 1.4 12 7.5

4 2.6 13 2.1

5 0.83 14 2.9

6 0.81 15 1.7

7 0.79 16 0.76

8 0.63 17 0.48

9 0.96 18 2.7

-20

-15

-10

-5

0

5

10

15

20

-40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30

PC

2

PC 1

Ace

Benefits Vitality

Benefits Immunity

Figure 3.9: Score plot of the PCA of the three multifruit juices measured using

the ETSPU (X-expl. (72%, 25%)).


A PCA was performed on the data of the ETSPU measurements of the

different fruit juice samples to explore the data. The analysis of the three

multifruit juices is shown in Figure 3.9. The three juices are clearly separated

from each other in the score plot. 97% of the variance is explained by the

first two PC’s. According to the correlation loadings (not shown), the first

PC is characterized by anionic sensors 1, 2, 3, 5, 6, 7 and 14, cationic sensors

9, 10, 11 and 17 and sensor 18, the pH sensor.

Figure 3.10 shows the results of the PCA on the data of the nine syrups

and mixtures. The syrups provided by Sunnyland and their mixtures are

grouped in a PCA using the ETSPU data. 80% of the variance is ex-

plained by the first two PC’s. Blend-9 fruit, orange, lemon and passion

fruit syrup are grouped together. An explanation, except for lemon syrup,

can be found in the composition of the blend-9 fruit syrup. This syrup is

made out of pineapple, orange, passion fruit, mandarin, grapefruit, banana,

mango, guava and papaya. Both orange and passion fruit are present in

high concentrations in the blend-9 fruit syrup. The close classification with

lemon syrup is probably caused by the similarities in the chemical compo-

sition of orange and lemon juice. Also, all four syrups are present in many

of the mixtures. Mixtures 1, 2, 4, 5, 6 and 7 are grouped together with the

four syrups. All of these mixtures contain high concentrations of the four

syrups. Mixture 8 is positioned in between orange syrup and apple syrup.

This mixture contains a high content of these two syrups. Red grape, el-

derberry, cherry and strawberry are grouped in two distinctive groups. In

between both groups, the mixtures which are prepared out of the four syrups

are positioned. Mixture 3, which resembles Benefits Immunity, has a fruit

composition similar to that of mixtures 9, 10 and 11, which explains its clas-

sification in the first quadrant of the score plot in between these mixtures.

The separation of red grape and strawberry from the other syrups along the

axis of the first PC is mainly caused by anionic sensors 3, 6, 14 and 16 and

cationic sensors 10 and 11.

82 3.3 Results

-100

-50

0

50

100

150

-100 -50 0 50 100 150 200P

C 2

PC 1

Blend-9 fruit Lemon OrangePassion fruit Apple Red grapeElderberry Cherry StrawberryMix 1 Mix 2 Mix 3Mix 4 Mix 5 Mix 6Mix 7 Mix 8 Mix 9Mix 10 Mix 11

Figure 3.10: Score plot of the PCA of the 9 syrups and 11 mixtures measured

using the ETSPU (X-expl. (59%, 21%)).

Finally, the three mixtures that were made based on information about

the fruit content of the three multifruit juices were analyzed together with

the three multifruit juices (Figure 3.11). 97% of the variance in the data is

explained using two PC’s. The three multifruit juices are clearly separated

from the three mixtures. All sensors, except for sensors 4, 8, 12, 13 and

18, seem responsible from this grouping along the axis of the first PC. The

separation is based on the difference in the composition of the samples. Since

the fruit content is the same in both the multifruit juice and the mixture

related with it, the separation is caused by the presence of other compounds,

like the vitamins, aloe vera puree and minerals (Table 3.3). Information and

ETSPU measurements of these extra components present in the multifruit

juices are thus necessary to make a good classification model based on the

fruit content.


-80

-60

-40

-20

0

20

40

60

80

100

-150 -100 -50 0 50 100 150 200

PC

2

PC 1Ace

Benefits Immunity Benefits

Vitality

Mix Ace

Mix Benefits Immunity

Mix Benefits Vitality

Figure 3.11: Score plot of the PCA of the three multifruit juices and the mixtures

with the same fruit content measured using the ETSPU (X-expl. (91%, 7%)).

After studying the potential of the ETSPU to group fruit juices, the abil-

ity of the system to quantify the syrup content of the samples was examined.

In PLS2 models the sensor readings were coupled to the information on the

syrup content found on the labels of the multifruit juices. The results are

shown in Table 3.19. The PLS2 model gives good results for almost all

nine syrups. The slopes and correlations of all calibration and validation

models are high and close to respectively one and 100%. Relatively large

errors, RMSE values, are found in the prediction of blend-9 fruit. This can

be explained by the complexity of this syrup. It is, as mentioned before,

composed out of nine different fruit. Some of the fruit were also analyzed

individually as syrups. The large errors in the prediction of apple have to

be seen relative to its concentration in the different multifruit juices. In

both Benefits Immunity and Vitality, the apple content is the highest of

84 3.3 Results

all individual fruit syrups. Despite the small amounts of lemon syrup and

passion fruit syrup, these fruit can be predicted perfectly in the multifruit

juices. This proves that the ETSPU is very accurate and can predict low

concentrations in a complex matrix.

Table 3.19: PLS2 models based on the ETSPU readings of the multifruit juices

(logarithm of percentage). Cross-validation was used to validate the model. The

offsets, RMSEC and RMSECV values are given in % v/v.

Syrup Slope Offset Correlation RMSEC

RMSECV

Blend-9 fruit Calibration 0.98 0.23 0.99 1.62

Validation 0.94 0.53 0.98 2.70

Lemon Calibration 0.99 0.004 0.99 0.05

Validation 0.95 0.007 0.98 0.09

Orange Calibration 0.99 0.007 0.99 0.39

Validation 0.99 0.03 0.99 0.62

Passion fruit Calibration 0.99 0.0006 0.99 0.03

Validation 0.99 0.002 0.99 0.05

Apple Calibration 0.98 0.58 0.99 3.38

Validation 0.94 2.86 0.98 5.64

Red grape Calibration 0.99 0.07 0.99 0.88

Validation 0.96 0.42 0.98 1.44

Elderberry Calibration 0.99 0.04 0.99 0.52

Validation 0.96 0.25 0.98 0.86

Cherry Calibration 0.99 0.01 0.99 0.14

Validation 0.96 0.07 0.98 0.23

Strawberry Calibration 0.99 0.02 0.99 0.19

Validation 0.96 0.09 0.98 0.32


3.4 Discussion



In a first experiment, dealing with two fruit species, the ability of the ET-

SPU to classify fruit based on their chemical composition and to quantify

their most important taste compounds was studied. Unstable sensors were

discarded from the data matrix before analysis, based on their CV value.

Since the same four sensors were considered unstable during both apple and

tomato analysis, it can be concluded that these sensors are not suitable for

the analysis of fruit samples. However, the biological variability which was

present in this experiment could have influenced the CV values.

The ETSPU and HPLC can separate fruit of different species. Apple and

tomato cultivars are separated clearly using the multisensor system, based on

their most abundant sugars and acids. Within one species, the used ETSPU

system is able to classify cultivars only when they are very different from

each other. Only Pinova and Aranca are clearly separated from the other

apple or tomato cultivars. The separations mainly occur based on differences

in the glucose, fructose and sucrose content of the samples. Sensors 1, 12, 19

and 21 are related to the sugars present in both apple and tomato according

to the different correlation loadings plots. This, however, is unexpected

since the ETSPU consists of potentiometric sensors. Potentiometric sensors

are not directly sensitive to sugars in solution. Some correlations between

the sugars and minerals in the samples might explain this result. The good

classification performance of the ETSPU complements the results on other

food products which are reported in literature by Di Natale et al. (2000);

Vlasov et al. (2002); Legin et al. (2005b)

Using an artificial juice, the ETSPU was evaluated as a tool for quan-

tification of individual compounds in complex solutions. The results of the

PLS1 regressions showed that the ETSPU is able to quantify citric acid and

malic acid correctly in mixtures of chemical compounds. The prediction

models of glucose are less good, which is in line with the fact that the ET

86 3.4 Discussion

is an array of potentiometric sensors. Prediction of individual compounds

in fruit samples seemed to be more difficult than in artificial juices. The

PLS2 results, which predict two acids and three sugars in apple and tomato

juices, indicate that no good predictions can be made. Two remarks must

be given at this point:

� The set of sensors used in this experiment was not optimized. As dis-

cussed by Legin et al. (1997), the choice of an optimized sensor array

is crucial for analysis. First, the chemical sensors have to be prepared

using the most well-known and promising classes of solution sensing

materials. This allows to find a high sensitivity to certain species and

significant chemical durability and signal stability of the sensors. Sec-

ond, the correct set of sensors needs to be selected. Based on profound

knowledge of dependencies of the sensing performance, a wide range

of original non-specific sensing materials with a high cross-sensitivity

might be obtained for different multicomponent liquids. The selection

of a set of sensors which is sensitive towards several compounds of in-

terest, could be done using response surfaces. In the next experiment,

an optimized sensor array was used to analyze tomato samples.

� The reference and ETSPU measurements were not performed on the

same final samples. The reference measurements performed on the ap-

ple and tomato samples were carried out using HPLC, which requires

extraction. The ETSPU measurements, on the other hand, were per-

formed on juices, without further sample pretreatment. In the fol-

lowing experiment discussing the comparison between two ET’s, the

same sample preparation was performed for both the reference mea-

surements and the ET analysis. Both the reference measurements and

the ET analyses were performed on juices. More reliable results re-

garding the quantification of chemical compounds are listed in the next

part.


3.4.2 Comparison between two electronic tongues

In the first experiment, the potential of the ETSPU was evaluated in an

experiment dealing with apple and tomato. The ETSPU proved to be able

to detect large differences in the composition of the samples. In this second

experiment, the potential of an optimized ETSPU was compared to that of

a commercially available system. The ASTREE ET has been successfully

used and is, according to the manufacturer, suitable for qualitative and

quantitative research purposes (Tan et al., 2001; AlphaM.O.S., 2006). An

overview of the most important differences between both ET systems is

shown in Table 3.20.

The multidimensional signals of both ET’s required deletion of unstable

sensors from the data matrix before statistical analysis could be performed.

One of the sensors deleted from the ETSPU is sensor 18, which is the pH

sensor. The instability of this sensor can be found in the material it is made

off. Since the sensor contains oxide glass it often shows some instability in

samples containing organic material, like for instance food samples (Legin,

2007). The sensors of the ASTREE ET developed by Alpha M.O.S. were

very sensitive to drift. Most probably this instability is due to the cleaning

method prescribed by the Alpha M.O.S. company (AlphaM.O.S., 2001a,b).

Instead of a thorough cleaning with different rinsing and washing steps like

the ETSPU, Alpha M.O.S. prescribes only a short rinsing of the sensors in

distilled water. Drift in sensor signals is often a severe problem in sensor

technology. Holmin et al. (2001) proposed some techniques to correct linear

drift in ET’s based on component correction and additive correction. The

Alpha M.O.S. company does not acknowledge the problem of drift and, thus,

prescribes the data to be analyzed as they are (AlphaM.O.S., 2006).

Large differences were obtained in the potential of both ET’s to classify

six tomato cultivars. The tomato cultivars in this experiment were chosen

so that a wide range of taste compounds was present between the samples.

This, together with the fact that an optimized set of sensors was used in the

ETSPU, made it possible for the system to classify the cultivars based on

their chemical composition. However, it is not possible to classify all samples

88 3.4 Discussion

within the correct cultivar. The correlation loading show that the separation

is, again, mainly based on the differences in glucose and fructose content

between the samples. Sensors 8, 9, 10 and 11 have a large response when

exposed to juice of the cultivar Amoroso, which is a cultivar containing high

concentrations of the two sugars and two minerals which were studied. This

implies that these four sensor readings are correlated with these compounds.

Finding a one-to-one relation between chemical compounds and sensors is

not possible. Moreover, this would contradict the cross-sensitivity aspect of

this type of sensor arrays (Legin et al., 1999a).

Compared to the ETSPU and reference techniques, the ASTREE ET

can classify the tomato samples correctly within each of the six cultivars.

However, despite the manufacturer’s promises, there is no proof that this

classification is based on the studied taste compounds. The separation of

two cultivars from the other four, could not be related to the sugar or acid

content of the samples. These classification results are in contrast to the

results of Bleibaum et al. (2002). This paper discusses the results of the

ASTREE ET analysis of a series of apple juices. Using the ASTREE ET,

apple juices can be classified based on their taste according to the author.

Since apples, in general, contain the same taste compounds as tomatoes

(Table 3.5), the results of the experiment of Bleibaum et al. (2002) should

be comparable to the experiment described in this thesis. In the experiment

of Bleibaum et al. (2002), however, a different set of sensors was used, i.e.

the commercially available set #2 (sensors ZZ, BB, CA, BA, AB, HA and

CB). The commercially available set #1 (sensors ZZ, BA, BB, CA, GA, HA

and JB) was chosen, based on their selectivity to sugars, acids and minerals

(AlphaM.O.S., 2001a,b, 2006), for the analysis of the tomato samples in this

experiment. The difference of two sensors in the arrays could explain the

incompatibility between the results found in literature and those described

in this thesis.

The ability of both multisensor systems to quantify the chemical content

of the samples was examined using PLS2 models in which the sensor read-

ings were coupled to the results of the reference measurements performed

on samples, which had undergone the same sample preparation. The ET-


SPU shows PLS2 models that can predict chemical compounds present in a

tomato matrix. Since the ETSPU contains potentiometric sensors, the good

prediction models of glucose and fructose are caused by correlations between

these compounds and the minerals present in the samples. Tomatoes with a

high sugar content in this experiment also have high mineral concentrations.

Validation of the prediction models on a completely independent dataset is

required in the future. The results of the ETSPU, both for classification

of cultivars and quantification of compounds, are better than the results of

the first experiment in which apple and tomato samples were analyzed. The

explanation for this is threefold:

� The sensors were optimized between the two experiments so that they

were better suited for the analysis of fruit samples.

� The analysis of the reference technique and the ET analysis are per-

formed on exactly the same samples.

� The tomato cultivars chosen for this experiment comprised a large

range, if not the largest range possible, of sugars and acids in tomato.

With this experiment, the ETSPU has proved its potential to classify cul-

tivars based on their chemical composition and quantify their taste com-

pounds. A large range of taste compounds was used. Since most tomato

and other fruit cultivars do not differ that much in chemical composition, it

would be advisable to perform an extra experiment, using this set of sensors,

to determine the sensitivity of the system.

The results of the PLS performed on the data from the ASTREE ET

are, as for the classification, completely different from those of the ETSPU.

The correlations between the measured and predicted concentrations are low

and all RPD values are below two, indicating that the models are not good.

From these results can be concluded that the ASTREE ET equipped with

this set of sensors is not able to predict individual chemical compounds in

tomato juices, despite the fact that Alpha M.O.S. advices this sensor array

for analysis of sugars, acids and minerals (AlphaM.O.S., 2001a,b, 2006).

90 3.4 Discussion

Table

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In previous experiments, the potential of the ETSPU and a commercially

available ET was studied. The third experiment evaluated the ETSPU for

use in quality control of fruit juices.

In a first step, unstable sensors were again identified based on the CV

values. None of the sensors of the ETSPU were sensitive to drift during the

experiment.

Second, a PCA was carried out to analyze the multidimensional data

structure measured with the ETSPU. On the score plot samples with abnor-

mal compositions were indentified. Since blend-9 fruit contains pineapple,

orange, passion fruit, mandarin, grapefruit, banana, mango, guava and pa-

paya, both orange and passion fruit are positioned close to blend-9 fruit in

the score plot of the PCA. Also, mixtures of syrups with a similar composi-

tion are located closer together than mixtures with different contents. This

systematic trend in taste evolution illustrates that the ETSPU has potential

for applications in food quality control and food adulteration. The sensitiv-

ity of the system was also illustrated in a second analysis where it was tried

to reassemble the original juices based on the individual syrups according

to the juice components indicated on the labels. It seemed impossible to

group the three multifruit juices together with the mixtures which have the

same fruit composition, because of the presence of other compounds, like the

vitamins, aloe vera puree and minerals in the multifruit juices (Table 3.3).

The ETSPU, as seen in the previous experiment, is very sensitive to miner-

als. Small differences in mineral content can already cause large differences

in the sensor readings. Information and ETSPU measurements of these ex-

tra compounds present in the multifruit juices are necessary to make good

models to determine abnormal fruit composition in commercially available

juices. Rudnitskaya et al. (2001) found that a difference of only 1% in the

water content of a fruit juice is detectable with the ETSPU. Detailed in-

formation on the extra compounds was not available, due to the company’s

policy on product secrecy.

92 3.4 Discussion

To validate the system for the determination of abnormalities in the pro-

duction of fruit juices, an extra experiment should be performed evaluating

the sensitivity of the sensors to small additions of a compound in a multifruit

juice. Legin et al. (2005b) studied the potential of the ETSPU in combi-

nation with PCA to determine the quality of vodka. The authors analyzed

samples from different producers both complying and not complying with

quality standards. Some samples contained higher than allowed quantities

of aldehydes, ethers, oils and/or n-propanol or were prepared with water

of bad quality. The ETSPU was found capable to distinguish vodka of dif-

ferent quality standards. The possibility to detect a lower or higher than

normal content of a syrup in the multifruit juices, has not been tested in

the experiment described in this thesis. By adding syrups to the multifruit

juices and measuring the resulting samples with the ETSPU, the sensitivity

of the system to detect small differences using PCA could be evaluated. Also

PLS regression techniques and multivariate statistical process control might

contribute to detect deviation from the regular multifruit juice composition.

Finally, PLS2 models were used to predict the exact concentrations of

fruit syrup in the multifruit juices. Such models could be used in quality

control of juice blending unit operations. From the results it is clear that,

using the ETSPU, it is possible to predict the fruit content of the multifruit

juices. The prediction of the blend-9 fruit and apple syrup is not as good as

the other syrups. This might be explained by the complexity of this syrup.

It is, as mentioned before, composed of nine different fruit. Despite the small

concentrations of lemon and passion fruit, these fruit are predicted perfectly

in the multifruit juices. This proves that the ETSPU is very sensitive and

can predict low concentrations in a complex matrix. Small differences in the

fruit content of a multifruit juice can be measured, which was the objective

of the experiment.

This preliminary experiment indicates that the ETSPU in combination

with multivariate statistical techniques is a powerful tool for the detection of

products of unacceptable or deviating quality, which has important practical

implications towards the food industry.


3.5 Conclusions

The potential of ET technology to classify food samples according to simi-

larity in their chemical content and to quantify their taste compounds was

studied in three experiments.

In a first experiment the ETSPU proved to be a good tool to classify

both apple and tomato cultivars based on large differences in their chemical

content. However, the ETSPU was not sensitive enough to accurately de-

tect individual sugars, and hence, discrimination between cultivars is mainly

based on the organic acid content and matrix effects. Artificial juices were

used to study the predictive ability of the multisensor system. Good results

were obtained for the prediction of both malic acid and citric acid. Predic-

tion of the sugar and acid content in the real apple and tomato samples using

this ET, however, is not possible using this set of sensors. This could be due

to the small concentration ranges or the difference in sample preparation

between the reference analysis and ETSPU measurements.

The potential of an optimized ETSPU was compared to a commercially

available system, the ASTREE ET developed by Alpha M.O.S., in a sec-

ond experiment. Both multisensor systems show considerable differences in

measurement protocol. The ETSPU demands little sample preparation and

only a relatively small amount of sample is needed. Cleaning of the sensors

takes more time, but because of this the sensors show almost no drift in

the time frame of the performed experiment. The commercially available

ASTREE ET requires a large amount of centrifuged sample. The sensor

cleaning protocol is rather limited and might result in sensor drift. Both

ET’s are able to classify tomato cultivars to some extend based on their

sugar, acid and mineral content. The ETSPU can quantify individual taste

compounds in a tomato matrix, while the ASTREE ET cannot quantify the

concentration of any of the studied compounds.

In a final experiment the potential of the ETSPU as a tool for quality

control was studied. Using this system it is possible to group multifruit juices

and fruit syrups. Information on the extra compounds present in the multi-

94 3.5 Conclusions

fruit juices is necessary to make a good PCA model to determine deviations

from the recipe of the multifruit juices. The ETSPU, however, can quantify

the fruit syrup content in the multifruit juices. Even low concentrations are

predicted very accurately in this complex matrix.

In this chapter, ET technology proved to be a good tool for identification,

classification, determination and quality control of fruit juices with different

chemical compositions. This indicates the possibilities of the system as an

instrument in the detection of fruit from different geographical origins or

orchards. The ET could be introduced in experiments dealing with the con-

servation of the quality of products and brands by the detection of artifacts

or spoilage. Using the ET it may also be possible to identify products with

different storage conditions or durations.

Chapter 4

Fourier transform infrared

spectroscopy

4.1 Introduction

Human evaluation has been the primary method of quality assessment, but

has many limitations. One of the large limitations of the human eye is

its use of only a very narrow band of the vast electromagnetic spectrum.

Some quality attributes, external and internal defects and compositional

factors are more readily detectable in the region outside the visible range,

e.g. ultraviolet (UV) and infrared (IR).

IR spectroscopy has been used extensively as an analytical technique to

gather information about both the structure and purity of a compound. The

IR region is divided into three regions: the NIR (8000 cm−1-4000 cm−1),

mid-IR (4000 cm−1-400 cm−1) and far-IR (400 cm−1-50 cm−1). Despite the

fact that mid-IR region is the region of greatest practical use to the organic

chemist, it has been less employed than the NIR region in food analysis.

Most foods contain large amounts of water that strongly absorbs mid-IR

radiation. The poor transmission and often high scattering of many sam-

ples means that usually very little light can be detected. The development

of Fourier transform infrared spectroscopy (FTIR) has renewed interest in

95

96 4.1 Introduction

the potential of mid-IR for food analysis. FTIR spectrometers using an in-

terferometer provide more energy to the sample, scan a lot faster and have

the capability to co-add data so that within a short time spectra can be

produced from poorly transmitting samples with acceptable signal-to-noise

ratios. Mid-IR has significant advantages over NIR for spectral assignment,

resolution and ease of quantification. Another advantage is that mid-infrared

spectra provide information about the physical and chemical states of indi-

vidual compounds (Wilson and Goodfellow, 1994; Griffiths and de Haseth,

2007). The development of FTIR has led to an increased interest in sample

presentation techniques. Today there is a wide choice of sample accessories

available with different designs and approaches (Gunasekaran, 2001; Grif-

fiths and de Haseth, 2007). Internal reflection, also known as attenuated

total reflectance (ATR), is one of the most powerful FTIR methods because

of its flexible sample presentation. ATR offers interesting possibilities for the

analysis of both solid and liquid samples. The determination of the authen-

ticity of fruit juices and differentiation of commercial juices based on sugars

and phenolic compounds was studied by He et al. (2007). ATR-FTIR suc-

cessfully resulted in the quantification of organic acids and sugars in apple

juices (Irudayaraj and Tewari, 2003) and caffeine in soft drinks (Paradkar

and Irudayaraj, 2002). In the last few years there has been a shift towards

flow injection FTIR analysis for the quantification of chemical compounds.

This will be discussed in detail in Chapter 5.

As literature shows, a lot of research has been performed to study the

potential of ATR-FTIR for the classification of samples and the determi-

nation of different compounds. Until now, however, no report has been

made regarding the use of ATR-FTIR to measure taste attributes of fruits.

A structured study, dealing first with the measurement of taste compounds

and second with taste as percieved by a sensory panel, will be a contribution

to ATR-FTIR research.

The objective of this chapter is to develop a high throughput technique

based on ATR-FTIR to classify fruit samples according to their most im-

portant taste compounds and to quantify these taste compounds. Hereto,

the potential of ATR-FTIR as a classification and quantification instrument

Fourier transform infrared spectroscopy 97

was studied using a wide variety of samples.

� In a first experiment the potential of ATR-FTIR to analyze taste com-

pounds will be studied. Different solutions of pure compounds and

mixtures will be measured with ATR-FTIR to determine the impor-

tant absorptions of IR light which are characteristic for each taste

compound. Calibration models will be built based on the absorbance

spectra of the pure compounds to predict their concentrations.

� Using both apple and tomato samples, the potential of ATR-FTIR

to classify apples and tomatoes according to their taste components

and to quantify their sugars and acids will be investigated in a sec-

ond experiment. The results will be compared to those of a reference

technique and the ETSPU.

� The potential of ATR-FTIR to classify three tomato cultivars and

quantify their taste compounds will be evaluated as a function of sam-

ple preparation technique. The samples are analyzed both as extracts

and juices. The results of these measurements will be compared to

those of an enzyme based reference technique.

� In a fourth experiment, dilutions of tomato juice and standard addi-

tions to the same samples will be analyzed with ATR-FTIR to build

calibration models based on a larger range of concentrations. With this

experiment, the ability of the system to quantify taste compounds will

be investigated thoroughly.

� Finally, in a last experiment, the potential of ATR-FTIR as a tool for

quality control will be studied since errors in the production process of

all food products should be detected as soon as possible. Different mul-

tifruit juices and the individual syrups they are made of are analyzed

using ATR-FTIR. The ability of this technique to detect differences

in the fruit composition of the multifruit juices will be studied. The

results are compared to those of the ETSPU.


and methods are described. The section discusses the samples used in the

98 4.2 Materials and Methods

different experiments and settings of the ATR-FTIR system in detail. Re-

sults of experiments on apples, tomatoes and multifruit juices are reported

in Section 4.3. In Section 4.4 the results are discussed and compared to refer-

ence measurements, ET results and literature findings. Concluding remarks

are formulated in Section 4.5. The results of the experiments described in

this chapter are published by Beullens et al. (2004, 2005a,b, 2006a) and

Rudnitskaya et al. (2006).

4.2 Materials and Methods

4.2.1 Samples

4.2.1.1 Taste compounds

Different concentrations of chemical components were analyzed using ATR-

FTIR in a experiment 1. The three sugars and two acids of interest are

glucose, fructose, sucrose, citric acid and malic acid. A sample with a high

content of one of the studied compounds was made in quadruple, to intro-

duce repetitions in the experiment, and then diluted with distilled water.

An extended dilution series was made for each compound with following

concentrations: 250 g/L, 200 g/L, 150 g/L, 100 g/L, 75 g/L, 50 g/L, 25

g/L, 20 g/L, 15 g/L, 10 g/L, 8 g/L, 6 g/L, 4 g/L, 2 g/L, 1 g/L, 0.75 g/L,

0.50 g/L, 0.25 g/L, 0.20 g/L, 0.15 g/L, 0.10 g/L, 0.075 g/L and 0.05 g/L.

Extremely high concentrations were analyzed next to low concentrations,

which are more realistic in fruit samples, to evaluate the potential of ATR-

FTIR to measure differences in concentrations. All samples were analyzed

immediately after preparation. The chemical compounds were purchased at

Sigma Aldrich (Steinheim, Germany).

Mixtures of three sugars and three acids, glucose, fructose, sucrose, citric

acid, malic acid and glutamate, were also prepared. Glutamate was added

in this experiment, since literature showed that this is an important taste

compound of tomato (Petro-Truza, 1987). To determine the concentrations


Table 4.1: Levels of fructose, glucose, sucrose, citric acid, malic acid and glutamate

used in the BB design (g/L).

Compound

Glucose 25 12.5 0

Fructose 25 12.5 0

Sucrose 2 1 0

Glutamate 3 1.5 0

Citric acid 8 4 0

Malic acid 2 1 0

of the compounds in the mixtures, a Box-Behnken (BB) design with six fac-

tors and three levels per factor was introduced (NIST/SEMATECH, 2007).

The BB calculations were performed in SAS version 9.1 (SAS Institute Inc.,

Cary, USA). Using this design, 54 samples were analyzed. The samples com-

prised six repetitions of the centerpoint. To this design, 16 extra samples

were added, to give a total of 70 samples to be analyzed. The extra samples

were mixtures which were not part of the original Box-Behnken design with

concentrations in between the ones determined in the original design. The

samples were added to include concentrations of acids and sugars which are

common in tomato. An overview of the levels of sugars and acids used in

the BB design is shown in Table 4.1. All mixtures were made at the same

moment and stored in falcon tubes at −80 ◦C until analysis. The samples

were analyzed in a random order. Each sample was analyzed 5 times using

ATR-FTIR.

4.2.1.2 Classification of apple and tomato cultivars and quantifi-

cation of taste compounds

Apples (Malus× domestica Borkh.) of five cultivars were used in experiment

2: Cox, Elstar, Golden Delicious, Jonagold and Pinova. The apples were

purchased at the local supermarket. Twenty apples per cultivar were cut into

pieces and frozen in falcon tubes using liquid nitrogen. The frozen samples

were stored at −80 ◦C until further sample preparation was performed.


Four tomato cultivars (Lycopersicon esculentum Mill.) were selected for

experiment 2: Aranca, Climaks, Clotilde and DRW 73-29. Twenty toma-

toes per cultivar were harvested at the Proefstation voor de Groenteteelt in


1975). The tomatoes were stored for one day at ambient atmosphere (18 ◦C

and 80% relative humidity). The day after harvesting the tomatoes were

cut into pieces, put into falcon tubes and frozen using liquid nitrogen. The


performed.

The apple and tomato samples were analyzed using ATR-FTIR, the

ETSPU (Chapter 3) and HPLC.

4.2.1.3 Extracted samples versus juices

Three tomato cultivars (Lycopersicon esculentum Mill.), Clotilde, Bonaparte

and Tricia, were analyzed in experiment 3. Ten tomatoes per cultivar were

obtained at the fruit- and vegetable Auction of Mechelen (Belgium) and the

Auction of Hoogstraten (Belgium). All fruit were picked at ripeness stage 5

(light red class) (USDA, 1975). Five tomatoes per cultivar were stored for

one day at ambient atmosphere, 18 ◦C and 80% relative humidity. The other

five fruit of each cultivar were stored for one week at the same conditions.

After storage for one day or one week, the tomatoes were cut into pieces

and frozen in falcon tubes using liquid nitrogen. The frozen samples were

stored at −80 ◦C until further sample preparation was performed.

Two different sample preparations were carried out on each sample.

Hereto, every sample was split in two. From one part an extract was pre-

pared (Chapter 3) and analyzed with an enzyme based reference technique

(EHT) and ATR-FTIR. The other part was centrifuged at 20000 g during 5

minutes (Hawk 15/05 Refrigerated centrifuge, Sanyo, Bensenville, USA) and

analyzed with EHT and ATR-FTIR. No HPLC analysis was performed on

the juices, since no reliable analysis is possible without sample extraction.


4.2.1.4 Dilutions and standard additions

Six tomato cultivars were chosen for an experiment (experiment 4) dealing

with dilution and standard addition to the samples: Macarena, Growdena,

Tricia, Admiro, Loredana and a cherry tomato. Ten tomatoes of the first

five cultivars were harvested at the Proefstation voor de Groenteteelt in


1975). The cherry tomatoes were purchased at the local supermarket. The

tomatoes were stored for one day at ambient atmosphere (18 ◦C and 80%

relative humidity). The day after purchase, the fruit were juiced and frozen

in falcon tubes using liquid nitrogen. The frozen samples were stored at

−80 ◦C until further sample preparation was performed.

Table 4.2: Composition of the mixtures used for standard addition (g/L).

Compound Mixture 1 Mixture2

Glucose 13.5 27

Fructose 14 28

Sucrose 5.5 11

Glutamate 5 10

Citric acid 11.5 23

Quinic acid 0.3 0.5

Malic acid 5.5 11

Tartaric acid 1.5 3

Just before measurement the samples were defrosted and divided into

four parts of 10 mL. One part of the juice was kept separately and analyzed

as such. A second part of the juice was diluted with distilled water to 1/2

of its original concentration. Finally, to the other two parts, a mixture of

sugars and acids was added. A stock solution of a mixture of pure chemical

components was prepared as shown in Table 4.2. The mixture and a 1/2

dilution of the mixture were added to the tomato samples. The chemical

compounds used for the standard addition were purchased at Sigma Aldrich

(Steinheim, Germany). After addition of the mixtures or dilution of the

juices, the samples were mixed thoroughly. All samples were analyzed using


ATR-FTIR. The diluted and the original samples were also analyzed using

the EHT reference method.

4.2.1.5 Quality control of fruit juices

Three multifruit juices of the brand Sunland (Sunnyland, Turnhout, Bel-

gium) were purchased at the local supermarket: Fruitdrink ACE, Benefits

Vitality and Benefits Immunity (experiment 5). The composition of the

multifruit juices, as mentioned on the label, is given in Chapter 3 (Table

3.3). The individual syrups and juices used to compose the multifruit juices

were provided by Sunnyland Distribution (Turnhout, Belgium): blend-9

fruit (fixed blend made out of pineapple, orange, passion fruit, mandarin,

grapefruit, banana, mango, guava and papaya), lemon, orange, passion fruit,

apple, red grape, elderberry, cherry and strawberry. The juices and syrups

were frozen in falcon tubes using liquid nitrogen and were stored at −80 ◦C

until further sample treatment. Using the individual syrups 11 mixtures

were made of which three resembled the composition of the multifruit juices.

Mixtures 1, 2 and 3 respectively correspond to Fruitdrink ACE, Benefits Vi-

tality and Benefits Immunity. The composition of all 11 mixtures is listed

in Chapter 3 (Table 3.4). In addition to these mixtures, syrup was added

to two of the multifruit juices. Passion fruit and cherry syrup were added

to respectively Benefits Immunity and Benefits Vitality with a syrup:juice

ratio of 1:9, 2:8, 3:7, 4:6 and 5:5. The nine individual syrups, the three

multifruit juices and the 11 mixtures were analyzed in triplicate using the

ETSPU (Chapter 3) and ATR-FTIR. The juices with addition of a syrup

were only analyzed using ATR-FTIR.

4.2.2 ATR-FTIR

Three different FTIR instruments were used in this thesis. An overview

of the instruments and settings is given in Table 4.3. All analyses were

performed on juices and centrifuged samples. The apple and tomato sam-

ples of experiment 2 were analyzed on a Bio-Rad FTS 6000 spectrometer


(Bio-Rad, Hercules, USA) at the Department of Agricultural and Biological

Engineering of Penn State University (State College, USA). The samples

were defrosted in a warm water bath at 25 ◦C and centrifuged at 20000 g

during 5 minutes (Hawk 15/05 Refrigerated centrifuge, Sanyo, Bensenville,

USA). One mL of the supernatants was put on a ZnSe crystal with 9 re-

flections for measurement. The individual chemical compounds (experiment

1) and the samples of experiments 3 and 4 were analyzed on a Bruker IFS

66v/S spectrometer (Bruker, Karlsruhe, Germany) at the Center for Sur-

face Chemistry and Catalysis of the K.U. Leuven (Leuven, Belgium). The

tomato juices were defrosted before analysis and centrifuged at 20000 g

during 5 minutes (Hawk 15/05 Refrigerated centrifuge, Sanyo, Bensenville,

USA). The measured extracts were prepared similarly as for the HPLC mea-

surements (Chapter 3). One mL of supernatants of the samples was put on

an AMTIR crystal with 9 reflections. The mixtures of experiment 1 and the

samples of experiment 5 were analyzed on a Bruker Tensor 27 spectrometer

(Bruker, Karlsruhe, Germany). All measurements were performed on cen-

trifuged juices. The juices were defrosted before analysis and centrifuged

at 20000 g during 5 minutes (Hawk 15/05 Refrigerated centrifuge, Sanyo,

Bensenville, USA). One mL of supernatants of the samples was put on the

AMTIR crystal. Temperature control was not included in the experimental

set-up, since the effects of long-term response instability can be eliminated

by using background spectra recorded immediately before or after the sam-

ple spectra in case of a short measurement time (MacBride et al., 1997).

Measurement time per sample is about 30 seconds for all FTIR instruments

at the settings used in this chapter. Between all measurements the ZnSe or

AMTIR crystal was carefully cleaned using distilled water and dried with a

special lens cleaning tissue (Schleichner and Schuell, Whatman International

Ltd., Maidstone, UK).


Table

4.3

:F

TIR

inst

rum

ents

an

dse

ttin

gs

use

dfo

rth

ean

aly

sis

of

diff

eren

tty

pes

ofsa

mp

les.

Equ

ipm

ent

Det

ecto

rA

TR

cell

Co-

adde

dR

esol

utio

nB

ackg

roun

dR

ange

Soft

war

e

scan

s

FT

S60

00B

io-R

adD

TG

SZ

nSe

644

cm−

1B

efor

eev

ery

4sa

mpl

es18

00-8

00cm

−1

Win

-IR

Pro

TM

2.5

IFS

66v/

SB

ruke

rM

CT

AM

TIR

128

4cm

−1

Bef

ore

ever

ysa

mpl

e18

00-9

00cm

−1

OP

US

5.5

Ten

sor

27B

ruke

rM

CT

AM

TIR

128

4cm

−1

Bef

ore

ever

ysa

mpl

e18

00-9

00cm

−1

OP

US

5.5


4.2.3 Reference techniques

4.2.3.1 HPLC

The apple and tomato samples were analyzed using HPLC. The sample

preparation and measurement of the sugar and acid content of the apple

and tomato samples was explained in detail in Chapter 3.


An enzymatic high throughput method, EHT, was used as a reference tech-

nique to evaluate the sugar and acid content of the tomato extracts and

juices. Details on the sample preparation and operational settings are given

in Chapter 3.


The ATR-FTIR data were preprocessed before the multivariate analysis.

The first derivative of the absorption spectra was calculated using the Savitsky-

Golay algorithm (second order polynomial, 5 points at each side). The first

derivative absorption spectra were used for further data analysis.

Multivariate data analysis was applied for both qualitative and quanti-

tative analysis using ATR-FTIR. Partial least squares-discriminant analysis

(PLS-DA) was used for data visualization and clustering of observations in

the data structure. Using this technique, intra-cultivar effects are minimized

and inter-cultivar effects are maximized. The analysis was performed on the

covariance matrix. Outliers were deleted from the analysis based on their

scores, leverages (distance to the model centre for each object summarized

over all components) and residuals (Geladi and Dabakk, 1995). The results

of the PLS-DA performed on the ATR-FTIR data were compared to those

of the reference techniques and ETSPU.

Principal component analysis (PCA), an unsupervised method, was used


as a data exploration technique on the data of the fruit juices, syrups and

mixtures. The potential of ATR-FTIR as a tool for quality control was

examined using this technique. The multifruit juices, syrups and their mix-

tures were grouped using PCA (Johnson and Wichern, 1992).

Partial least squares analysis (PLS) was performed to study the pre-

dictive capacity of ATR-FTIR for individual compounds and syrups. The

calibration models were validated using cross-validation, where a small set

of randomly selected samples is left out of to construct the model and is

used afterwards for validation purposes. The limit of detection (LOD) was

calculated from the 95% confidence interval for each compound, both in a

watery solution and in the tomato matrix. The 95% confidence interval is

expressed as ± 1.96RMSEC. PLS2 was used for the prediction of the sug-

ars and acids in apples, tomatoes and the prediction of syrups in multifruit

juices. In a PLS2 analysis, all compounds of interest (Y variables) are re-

lated together to the absorbances (X variables). The results of the HPLC

and/or EHT measurements were taken as references for the assessment of

individual chemical compounds in apple and tomato. For the prediction of

individual compounds in experiment 1, PLS1 was used. In this case, the

concentration of the sugars and acids are related one by one (one Y vari-

able) to the absorbances (X variables) (Martens and Naes, 1998). Prediction

models having a correlation above 0.90, high slope and low offset were con-

sidered to be good. The correlation gives information about the quality of

the model, but gives no direct information about the prediction accuracy.

The RPD value is a factor which indicates the accuracy. An RPD value be-

tween 2 and 2.5 makes approximate quantitative predictions possible. For

values between 2.5 and 3, and above 3, the prediction is classified as good

and excellent, respectively (Saeys et al., 2005).

For data analysis two different computer software programs were used:

The Unscrambler version 9.1.2 (CAMO Technologies Inc., Olso, Norway)

and SAS version 9.1 (SAS Institute Inc., Cary, USA).


4.3 Results

4.3.1 Taste compounds

The spectra of the three sugars and two acids in a concentration of 50

g/L are shown in Figure 4.1. The spectra of the three sugars show a lot of

similarities. Glucose, fructose and sucrose do not absorb significant amounts

of IR light between 1800 cm−1 and 1475 cm−1. The highest absorption bands

were found between 1170 cm−1 and 900 cm−1. Differences in the absorbance

peaks of the three sugars are visible in this part of the spectrum. Glucose

shows separated peaks, with two clearly defined peaks at 1080 cm−1 and 1034

cm−1. The spectrum of fructose is a lot smoother in this area, compared to

glucose, with only small peaks and one large peak at 1063 cm−1.

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

1799

1753

1707

1660

1614

1568

1522

1475

1429

1383

1336

1290

1244

1198

1151

1105

1059

1012 966

920

Abs

orba

nce

Wavenumber (cm-1)

Glucose FructoseSucrose Citric acidMalic acid

Figure 4.1: ATR-FTIR absorbance spectra of glucose, fructose, sucrose, citric

acid and malic acid.

108 4.3 Results

The spectrum of sucrose has a different shape between 1170 cm−1 and

900 cm−1. There are three clear peaks and one zone with an overlap of two

peaks, which make the spectrum of sucrose unique. The characteristic peaks

for sucrose are at 1055 cm−1, 997 cm−1 and 924 cm−1.

The spectra of the two acids show very different absorptions compared

to the sugars. The spectra of citric acid and malic acid have a peak at

1722 cm−1. Citric acid furthermore shows a high absorbance around 1223

cm−1. The spectrum of malic acid shows three overlapping peaks between

1321 cm−1 and 1140 cm−1 and a defined peak at 1109 cm−1. The region

of the spectrum between 1500 cm−1 and 900 cm−1, is called the fingerprint

region. Peaks in this region are very difficult to assign to specific molecular

vibrations. However, this complexity has an important advantage in that it

can serve as a fingerprint for a given compound. Consequently, by referring

to known spectra, the fingerprint region can be used to identify a compound.

C C C C CH C H

H OH H

OH

OO

HO

HO O

OH

OO

HOC C C C

H H

H OH

C C H

HCH

CHC O

H

OH

OH

OHHO

CH2OHCitric acid Malic acid

H

H

HO

OH

HO C C

C

O

CH

CH2OH

HOH2C

Fructose Glucose

OHC C H

HCH

CHC O

HOH

HO

CH2OH

H

H

HO

OHC C

C

O

CH

CH2OH

HOH2C

OSucrose

Figure 4.2: Chemical structures of glucose, fructose, sucrose, citric acid and malic

acid.


In Table 4.5 and Table 4.4 an overview of the wavenumbers at which

absorbance peaks are present in the spectra of glucose, fructose, sucrose,

citric acid and malic acid. Also, the specific vibrations that absorb at these

wavenumbers are reported. Figure 4.2 shows the chemical structures of the

five compounds of interest.

Table 4.4: Wavenumbers and vibrations with high absorbances in two acids (Grif-

fiths and de Haseth, 2007).

Compound Wavenumber Vibration

Citric acid 1722 cm−1 C=O stretching

1400 cm−1 CH bending OH bending

1317 cm−1 C-O stretching OH bending

1223 cm−1 C-O stretching


1078 cm−1 C-O stretching C-C stretching

1057 cm−1 C-C stretching

Malic acid 1722 cm−1 C=O stretching

1400 cm−1 CH bending OH bending


1275 cm−1 CH2 rocking C-O stretching


1190 cm−1 CH2 wagging C-O stretching



110 4.3 Results

Table 4.5: Wavenumbers and vibrations with high absorbances in three sugars

(Griffiths and de Haseth, 2007).

Compound Wavenumber Vibration

Glucose 1452 cm−1 CH2 scissoring

1422 cm−1 CH2 scissoring CH bending










Fructose 1452 cm−1 CH2 scissoring













Sucrose 1452 cm−1 CH2 scissoring












The absorbance spectra of the dilution series between 250 g/L and 10

g/L of glucose is shown in Figure 4.3. The sample corresponding to the

spectrum with the highest absorption has the highest glucose concentration.

0.4

0.5

0.6

0.7

ce

250 g/L200 g/L150 g/L100 g/L75 g/L50 g/L25 g/L20 g/L

-0.1

0

0.1

0.2

0.3

1799

1753

1707

1660

1614

1568

1522

1475

1429

1383

1336

1290

1244

1198

1151

1105

1059

1012 96

6

920

Abso

rban

c

Wavenumber (cm-1)

20 g/L10 g/L

Figure 4.3: ATR-FTIR absorbance spectra of a dilution series between 250 g/L

and 10 g/L of glucose.

Using the ATR-FTIR first derivative spectra of the pure compounds,

PLS1 models were made to predict the concentration of a chemical com-

pound in a sample. PLS1 was preferred over PLS2 to make the prediction

models since only one compound was present in each of the measured sam-

ples. The dilution series between 50 g/L and 0.5 g/L was used to calibrate

the model since these concentrations are the most realistic to be found in

real fruit samples. PLS1 models were built using different variable selection

methods.

112 4.3 Results

Table 4.6: PLS1 validation models to predict individual compounds built on the

results of the ATR-FTIR measurements of pure compounds. Cross-validation was

used to validate the model. (Model 1: full spectrum; Model 2: two selected regions;

Model 3: 20 selected wavenumbers; Model 4: two selected wavenumbers)

Compound Model 1 Model 2 Model 3 Model 4

Malic acid R 0.99 0.99 0.99 0.99

RPD 27 37 33 9.2

Citric acid R 0.99 0.99 0.99 0.99

RPD 9.7 8.8 11 9.4

Sucrose R 0.99 0.99 0.99 0.99

RPD 39.8 35 35 7.0

Glucose R 0.99 0.99 0.99 0.99

RPD 18 25 28.5 5.8

Fructose R 0.99 0.99 0.99 0.99

RPD 14 21 22 5.1

The first model was built using the full first derivative spectrum. One PC

was selected to obtain the results shown in Table 4.6. High correlations and

RPD values are found for all compounds. The second model was built based

on the first derivative absorbances in two important regions of vibrations:

1800 cm−1 to 1650 cm−1 and 1140 cm−1 to 950 cm−1 resembling the main

absorbance regions of C=O vibrations of organic acids and C-O stretching

vibrations of carbohydrates respectively. One PC was used for all models.

The results of this model are equally good or better than those of the first

model. The RPD values of all prediction models are high, referring to a

good prediction performance. The good results of this model indicate that

no information necessary for the prediction of a compound was deleted from

the analysis. A third model was built using 20 wavenumbers per compound

which were selected as the minima and maxima of the first derivative spectra.

The selected wavenumbers are the inflection points of the main peaks of the

spectra. The results are better or equal to those of the previous models.


Table

4.7

:T

wo

sele

cted

wav

enu

mb

ers

per

chem

icalco

mp

ou

nd

use

dto

bu

ild

PL

S1

mod

els.

Com

poun

dSe

lect

edw

aven

umbe

rIn

flect

ion

poin

tV

ibra

tion

ofw

aven

umbe

r

Mal

icac

id17

66cm

−1

1722

cm−

1C

=O

stre

tchi

ng

1309

cm−

112

75cm

−1

C-O

stre

tchi

ngan

dC

H2

rock

ing

Cit

ric

acid

1367

cm−

114

00cm

−1

CH

and

OH

bend

ing

1243

cm−

112

23cm

−1

C-O

stre

tchi

ng

Sucr

ose

1155

cm−

111

34cm

−1

C-O

stre

tchi

ng

1062

cm−

110

55cm

−1

C-O

and

C-C

stre

tchi

ng

Glu

cose

1087

cm−

110

80cm

−1

C-O

and

C-C

stre

tchi

ng

1056

cm−

110

34cm

−1

C-O

and

C-C

stre

tchi

ng

Fruc

tose

1467

cm−

114

52cm

−1

CH

2sc

isso

ring

1074

cm−

110

63cm

−1

C-O

and

C-C

stre

tchi

ng

114 4.3 Results

A fourth model was made using only two wavenumbers per compound.

The two wavenumbers were selected from the maxima and minima of the

first derivative absorbance spectrum and the correlation loadings of these

wavenumbers. The selected wavenumbers are given in Table 4.7. One PC

was used in each PLS1 model. High correlations between the predicted and

known concentrations were found. The RMSECV values of all compounds

are higher than in the PLS1 analysis using the absorbance at all wavenum-

bers. The RPD values of all prediction models are lower than those of the

previous models, however, they are all still higher than 2. This indicates

that, using only two unique variables, a model can be made to predict a

compound using ATR-FTIR. Since absorbance spectra are influenced by

the presence of all compounds in a mixture, it is best to select more than

two variables per compound to make accurate predictions. From these four

models can be concluded that the selection of wavenumbers increases the pre-

dictive ability of ATR-FTIR. The best predictions, with the highest RPD’s

for all compounds, were found when 20 peaks were selected from the first

derivative spectra. Using this model, the LOD was calculated for each of

the compounds. Table 4.8 shows that the LOD’s are not very low, indicat-

ing that the system can only be used for the determination of compounds

present in high concentrations.

Table 4.8: Limit of detection (g/L) of taste compounds in pure solutions based

on calibration model 3.

Compound LOD

Fructose 1.6

Glucose 1.2

Sucrose 0.9

Citric acid 2.4

Malic acid 1.2


-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

1799

1753

1707

1660

1614

1568

1522

1475

1429

1383

1336

1290

1244

1198

1151

1105

1059

1012 966

920

Abs

orba

nce

Wavenumber (cm-1)

Mixture SumGlucoseFructoseSucrose

Figure 4.4: ATR-FTIR absorbance spectra of a mixture of taste compounds and

the sum of their individual spectra.

To illustrate the additive aspect of FTIR, mixtures of sugars and acids

were analyzed. Figure 4.4 shows the absorbance spectra of one of the mix-

tures together with the sum of the spectra of the individual compounds.

PLS2 prediction models were built based on the minima and maxima

of the first derivative spectra of the mixtures. The results are not shown

because of their similarity with the previous results. High correlations, with

values of 0.99, were found for the prediction models for all compounds.

The RMSECV values are low, indicating low prediction errors, and the

RPD’s reach high values. Thus, ATR-FTIR is also able to predict individual

chemical compounds when they are present in mixtures.

116 4.3 Results



4.3.2.1 Classification with ATR-FTIR

The results of the reference measurement with HPLC performed on apple

and tomato samples were already presented in Chapter 3.

0.20

0.25

0.30

0.35

ce

Cox Elstar

Golden Jonagold

Pinova Aranca

Climaks Clotilde

DRW 73-29

-0.05

0.00

0.05

0.10

0.15

799

845

891

937

984

1030

1076

1123

1169

1215

1261

1308

1354

1400

1447

1493

1539

1585

1632

1678

1724

1771

Abso

rban

c

Wavenumber (cm-1)

Figure 4.5: Average ATR-FTIR absorbance spectra of five apple and four tomato

cultivars.

Figure 4.5 shows the average absorbance spectra for the five apple and

four tomato cultivars. The differences in the absorbance spectra are related

to differences in the chemical composition of the fruit (Table 3.5). A major

difference in the absorbance spectra of the apple cultivars is observed in the

wavenumber range between 1200 cm−1 and 900 cm−1. In this range both

Cox and Elstar show higher absorbance of IR light than the other three ap-


ple cultivars, which is related to C-O stretching vibrations that are present

mainly in sugars and to a lesser extend in acid. The results of the HPLC

measurements confirm that both cultivars do not have a high content of

those sugars. Cox, however, has a high content of sucrose, the main sugar

in apples, which absorbs more IR light than the other sugars around 930

cm−1. Pinova absorbs more around 1105 cm−1 and 1078 cm−1. The neg-

ative absorbance in the region between 1680 cm−1 and 1580 cm−1 results

from the background measurement of water. Water absorbs highly in this

area due to bending vibrations in water molecules which have shifted be-

cause of hydrogen bindings (Venyaminov and Prendergast, 1997; Garrigues

et al., 2000). Since the amount of water is different in the samples and the

background, a negative absorption is observed in the spectra. From the first

derivative absorbance spectrum (Figure 4.6), 20 wavenumbers were selected

for further data analysis. The selected wavenumbers are listed in Table 4.9.

0.004

0.008

0.012

e ab

sorb

ance


-0.012

-0.008

-0.004

0.000

799

845

891

937

984

1030

1076

1123

1169

1215

1261

1308

1354

1400

1447

1493

1539

1585

1632

1678

1724

1771

Firs

t der

ivat

ive

Wavenumber (cm-1)

Figure 4.6: Average first derivative absorbance spectra of five apple cultivars.

118 4.3 Results

Table 4.9: Selected wavenumbers for apple and tomato based on the first derivative

absorbance spectra (Exp2: apple and tomato samples; Exp3: extracted and juiced

tomato samples; Exp4: tomato samples with standard additions).

Exp2 Exp2 Exp3 Exp4

Apple Tomato

1467 cm−1 1309 cm−1 1743 cm−1 1745 cm−1

1448 cm−1 1269 cm−1 1465 cm−1 1700 cm−1

1400 cm−1 1222 cm−1 1448 cm−1 1608 cm−1

1271 cm−1 1165 cm−1 1425 cm−1 1465 cm−1

1228 cm−1 1145 cm−1 1396 cm−1 1423 cm−1

1182 cm−1 1114 cm−1 1367 cm−1 1390 cm−1

1114 cm−1 1101 cm−1 1338 cm−1 1367 cm−1

1101 cm−1 1087 cm−1 1268 cm−1 1338 cm−1

1089 cm−1 1074 cm−1 1222 cm−1 1267 cm−1

1070 cm−1 1055 cm−1 1199 cm−1 1220 cm−1

1053 cm−1 1041 cm−1 1164 cm−1 1164 cm−1

1041 cm−1 1028 cm−1 1145 cm−1 1145 cm−1

1028 cm−1 1010 cm−1 1114 cm−1 1114 cm−1

1001 cm−1 993 cm−1 1101 cm−1 1101 cm−1

981 cm−1 981 cm−1 1089 cm−1 1087 cm−1

960 cm−1 960 cm−1 1074 cm−1 1068 cm−1

937 cm−1 914 cm−1 1056 cm−1 1056 cm−1

914 cm−1 889 cm−1 1043 cm−1 1041 cm−1

875 cm−1 875 cm−1 1012 cm−1 1018 cm−1

860 cm−1 860 cm−1 993 cm−1 993 cm−1

Despite the fact that the tomato samples absorb less than the apples,

large differences are observed between the four cultivars. Aranca absorbs

more light than the other cultivars in the whole wavenumber range studied.

The largest differences are found in the area between 1200 cm−1 and 900

cm−1, which is the main area of interest for sugars and acids because of

the strong C-O stretching vibrations. Aranca shows higher absorption as

a result of the high content of three sugars in this cultivar. The results of

the HPLC measurements show that Aranca contains high concentrations of

sucrose, glucose and fructose. Clotilde shows higher absorbance than the


other two cultivars in the range between 1458 cm−1 and 885 cm−1. The

HPLC data confirm that Clotilde has the second highest content of glucose

and fructose. The content of both sugars in Clotilde is higher than that in

Climaks and DRW 73-29. From the first derivative absorbance spectrum,

20 wavenumbers were selected for further data analysis (Table 4.9).

0 000

0.002

0.004

0.006

0.008

PC

2

PC 1

Apple

Tomato

-0.008

-0.006

-0.004

-0.002

0.000-0.015 -0.010 -0.005 0.000 0.005 0.010 0.015

Figure 4.7: Score plot of the PLS-DA of the apple and tomato samples measured

using ATR-FTIR (X-expl. (79%, 15%); Y-expl. (96%, 1%)).

A PLS-DA was performed on the correlation matrix of the data from

the ATR-FTIR analysis to classify the apple and tomato cultivars based

on their absorbance spectra. The apple and tomato samples are separated

from each other in the score plot (Figure 4.7). The variability within the

apple samples is larger than that within the tomato samples. The tomatoes

are positioned in a line. This line, however, does not represent a time shift

of the instrument detector but a shift according to the cultivar. The line

occurs due to the fact that a large variability is present in the apple samples

120 4.3 Results

compared to the tomato samples. Within each group, apples and tomatoes,

the samples are grouped per cultivar. Using ATR-FTIR it is thus possible

to discriminate between species.

The results of the PLS-DA performed on the apple data are presented

in Figure 4.8. Cox and Elstar are clearly separated from each other and the

other apple cultivars along the axis of PC 1. Both cultivars are located in

the left quadrants of the plot. In the correlation loadings plot two ellipses

are visible. The inner and outer ellipses on the figures represent correla-

tion coefficients of 70% and 100% (or R2 values of 50% and 100%). For a

wavenumber located between the two ellipses more than 70% of its variabil-

ity is explained by the first two PC’s. This means this variable is important

in describing the variability, and, hence, the major cultivar effects, within

the data set. The classification along the axis of PC 1 is mainly caused by

wavenumbers 1271 cm−1, 1182 cm−1, 1115 cm−1, 1101 cm−1, 1090 cm−1,

1042 cm−1, 1001 cm−1, 982 cm−1 and 914 cm−1 in the first derivative spec-

tra. Cox is highly correlated with these wavenumbers. They are all situated

in the region of strong C-O stretching vibrations of sugars in the absorbance

spectrum. As mentioned before, Cox and Elstar show high absorptions in

the region between 1200 cm−1 and 900 cm−1. Golden and Pinova are posi-

tioned in the upper and lower right quadrants of the score plot, respectively,

but do both overlap with the Jonagold samples. Pinova shows high ab-

sorbances around 1100 cm−1. In this region C-O vibrations occur, which

are related to a high content of glucose and fructose in this cultivar. Pinova

is correlated to 1229 cm−1, 1053 cm−1, 1028 cm−1 and 961 cm−1. From

the PLS-DA correlation loadings can be observed that the vibrations in the

region related to sugar content are very important for the separation be-

tween Cox and Pinova. Similar differences in sucrose content were found

in the HPLC data. The classification results (Table 4.10) show that using

ATR-FTIR all samples of these two cultivars are classified correctly. The

results of the analysis with ATR-FTIR differ a lot from those of the ETSPU.

Using the multisensor system, it was not possible to classify the samples by

cultivar and to distinguish between the apple cultivars except for Pinova.


-0.004

-0.002

0.000

0.002

0.004

-0.006 -0.004 -0.002 0.000 0.002 0.004 0.006P

C 2

PC 1


A

876937

1070

1090

1101

1115

1182

1271 1449

1468

Elstar

Golden

Jonagold0.2

0.4

0.6

0.8

1.0

PC

2

860914

961982

1001

1028

10421053

1101

1229

1400

CoxPinova

-1.0

-0.8

-0.6

-0.4

-0.2

0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

PC 1

B


the apple samples measured by ATR-FTIR (X-expl. (79%, 16%); Y-expl. (22%,

10%)).

122 4.3 Results


and ATR-FTIR measurements on apple and tomato. The percentage of correct

classified samples is shown (%).

Cultivar HPLC ATR-FTIR

Cox 100 100

Elstar 75 90

Jonagold 85 70

Golden 35 80

Pinova 60 100

Aranca 100 95

Climaks 90 95

Clotilde 65 60

DRW 73-29 85 80

A similar analysis was carried out on the tomato samples. The results

are visualized in Figure 4.9. Aranca is clearly separated from the other three

cultivars along the axis of the first PC, which is the same result as found

using the ETSPU. Using a supervised method to classify the cultivars, Cli-

maks and DRW 73-29 are separated from each other along the axis of the

second PC. According to the correlation loadings this separation is caused

by wavenumber 1074 cm−1. Clotilde is positioned in between both previ-

ously mentioned cultivars along the axis of the second PC. The absorption

at this wavenumber is an important indication of the fructose content of

the samples. The absorbance spectra of Climaks and DRW 73-29, however,

show complete overlap in the region around this wavenumber while the ref-

erence measurements indicate differences in fructose content between both

cultivars. Climaks, Clotilde and DRW 73-29 are not directly correlated to

any of the selected wavenumbers. Using the ETSPU, Climaks, Clotilde and

DRW 73-29 are not separated from each other. This is also reflected in the

percentage of correctly classified samples. Using ATR-FTIR, respectively

95%, 60% and 80% of the samples of Climaks, Clotilde and DRW 73-29 are

classified correctly (Table 4.10). While, as shown in Chapter 3, only 60%,

15% and 10% of the same samples are classified within the correct cultivar

using the ETSPU.


-0.0003

-0.0002

-0.0001

0.0000

0.0001

0.0002

0.0003

-0.003 -0.002 -0.001 0.000 0.001 0.002 0.003 0.004

PC

2PC 1

Aranca

Climaks

Clotilde

DRW 73-29

A

860876

889

914961

982

993

1011

1028

1042

1055

1074

1088

1101

1115

11461165 1223

1269

1310Aranca

Climaks

Clotilde

DRW 73-29

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

PC

2

PC 1

B


the tomato samples measured by ATR-FTIR (X-expl. (98%, 0%); Y-expl. (28%,

23%)).

124 4.3 Results

4.3.2.2 Quantification with ATR-FTIR

A PLS2 analysis was performed based on the ATR-FTIR spectra of both

the apple and tomato samples. Table 4.11 presents the results of the PLS2

prediction model for the tomato samples. The results of the prediction

models of apple are not shown because of their similarity to the results

found for tomato. Based on the RMSEC values, the LOD was calculated

for each compound in the tomato matrix. Table 4.12 shows that all LOD’s

are below the values found in the tomato samples, which indicates that all

compounds can be detected using this method. The correlations between

the compounds present in the apple and tomato samples and the ATR-FTIR

absorbance spectra are low. For tomato, the ’best’ PLS models are made for

malic acid, glucose and fructose. The correlations of the calibration models

are respectively 88%, 89% and 88% for malic acid, glucose and fructose.

Both the calibration and validation model of all three compounds are very

similar. The RMSEC and RMSECV values of calibration and validation

model for malic acid are very low. Despite this, the RPD value of the

malic acid model is low. While an RPD value below 1.5 indicates that the

model is not usable, an RPD value between 1.5 and 2 reveals a possibility

to distinguish between high and low values. The models for glucose and

fructose have RPD values of respectively 2.1 and 2.1, which indicates that

approximate predictions are possible for these sugars.

4.3.3 Extracted samples versus juices

4.3.3.1 Classification with EHT

To introduce a larger range in sugar and acid content in the samples, shelf

life was introduced in this experiment. No significant differences, however,

were found between the samples which were stored for one day and one

week at ambient atmosphere (results not shown). This is contradictory to

storage physiology, where the content of sugars should increase during shelf

life. A shelf life of one week, however, does not introduce singificant changes

in sugar and acid profile. The effect of shelf life will therefore be discarded


Table 4.11: PLS2 models to predict individual compounds measured by HPLC

in tomato samples built on the results of the ATR-FTIR measurements. Cross-

validation was used to validate the model. The offsets, RMSEC and RMSECV

values are given in mg/g powder.


RMSECV


Validation 0.76 0.02 0.86 0.03 1.8


Validation 0.42 0.90 0.61 0.31 1.3


Validation 0.47 0.31 0.67 0.15 1.4


Validation 0.77 0.36 0.87 0.21 2.1


Validation 0.76 0.37 0.86 0.17 2.1

Table 4.12: Limit of detection (mg/g powder) of taste compounds in a tomato

matrix.

Compound LOD

Fructose 0.29

Glucose 0.37

Sucrose 0.27

Citric acid 0.55

Malic acid 0.06

126 4.3 Results

in the rest of the analysis. The EHT measurements of the extracted and

juiced tomato samples give similar results of the content of taste compounds.

Bonaparte contains high amounts of both sugars and malic acid. The malic

acid content is higher than that of the other cultivars. The content of citric

acid in this cultivar is lower than that of the Clotilde. Tricia contains the

lowest concentrations of glucose and fructose.

Fructose

Citric acidClotilde

0 0

0.2

0.4

0.6

0.8

1.0

PC

2

PC 1Glucose

Malic acid

Glutamate

Bonaparte

Tricia

-1.0

-0.8

-0.6

-0.4

-0.2

0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

PC 1

Figure 4.10: Correlation loadings plot of the PLS-DA of the extracted tomato

samples measured by EHT (X-expl. (91%, 6%); Y-expl. (37%, 41%)).

A PLS-DA was performed on the data of the EHT analysis of both sam-

ple preparations. Figure 4.10 shows the results for the extracted samples.

The separation of the three tomato cultivars appears along the axes of the

first two PC’s. In the correlation loadings plot shows that Bonaparte is

correlated with malic acid, while Clotilde is correlated with citric acid and

Tricia is negatively correlated with glucose, fructose and glutamate. The

same PLS-DA performed on the EHT data of the juices (Figure 4.11) shows

similar results. The results of the reference measurements for both sample

preparation techniques are similar. It can be concluded that both the ex-

tracts and juices might be used for reference analysis (Vermeir et al., 2007).


Since the juices demand less sample preparation, in future experiments juices

will be used.

Glucose

Fructose

Malic acid

Citric acid

Glutamate

Bonaparte

Clotilde

Tricia

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

PC

2

PC 1

Figure 4.11: Correlation loadings plot of the PLS-DA of the tomato juices mea-

sured by EHT (X-expl. (91%, 6%); Y-expl. (36%, 22%).

4.3.3.2 Classification with ATR-FTIR

The average absorbance spectra of the extracts and juices of the three tomato

cultivars are given in Figure 4.12. The absorbance spectra of the extracts

are lower than those for the juices. This means a large amount of chemical

compounds is lost during preparation of the extracts. The lower absorbances

also result from the dilution which occurs during the preparation of the

extracts. Tricia absorbs less than the other two cultivars, both as a juice

and an extract, due to its low content of glucose, fructose, malic acid and

glutamate.

128 4.3 Results

0.00

0.02

0.04

0.06

0.08

0.10

0.12

1797

1751

1705

1659

1612

1566

1520

1473

1427

1381

1335

1288

1242

1196

1149

1103

1057

1011 964

918

Abs

orba

nce

Wavenumber (cm-1)

Tricia-juiceTricia-extractBonaparte-juiceBonaparte-extractClotilde-juiceClotilde-extract

Figure 4.12: Average ATR-FTIR absorbance spectra of three tomato cultivars

with two types of sample preparation.

Twenty wavenumbers were selected from the first derivative absorbance

spectra for further data analysis. The selected wavenumbers are the same

for both the extracted samples and the juices (Table 4.9). Compared to the

previous experiment, more wavenumbers in the region of CH2 scissoring, CH

bending and OH bending were chosen. This can be explained by the fact

that less noise is present in the spectra compared to experiment 2. Different

FTIR spectrometers, horizontal ATR crystals and number of co-added scans

were used for both experiments (Table 4.3). The selected wavenumbers in

the region with high absorption between 1200 cm−1 and 990 cm−1 are similar

in both experiments.


-0.0003

-0.0002

-0.0001

0.0000

0.0001

0.0002

0.0003

0.0004

-0.0004 -0.0003 -0.0002 -0.0001 0.0000 0.0001 0.0002 0.0003 0.0004

PC

2PC 1

Tricia

Bonaparte

Clotilde

Figure 4.13: Score plot of the PLS-DA of the extracted tomato samples measured

by ATR-FTIR (X-expl. (15% 13%); Y-expl. (31%, 11%)).


and ATR-FTIR measurements on the tomato extracts and juices. The percentage

of correct classified samples is shown (%).

Cultivar EHT ATR-FTIR

Extract Juice Extract Juice

Bonaparte 100 100 100 100

Clotilde 90 70 70 90

Tricia 100 80 80 90

130 4.3 Results

-0.0005

-0.0003

-0.0001

0.0001

0.0003

0.0005

-0.002 -0.001 0.000 0.001 0.002

PC

2

PC 1

Tricia

Bonaparte

Clotilde

Figure 4.14: Score plot of the PLS-DA of the tomato juices measured by ATR-

FTIR (X-expl. (84%, 3%); Y-expl. (42%, 37%)).

The PLS-DA performed on the absorbances at the 20 selected wavenum-

bers of the ATR-FTIR measurements of the extracted samples (Figure 4.13)

shows that the samples of the three cultivars overlap quite a lot. Tricia and

Bonaparte can be separated from each other along the axis of PC 1, but

Clotilde shows overlap with both cultivars. No strong correlations are found

between the selected wavenumbers and the tomato cultivars. The classifi-

cation results, however, show that respectively 100%, 70% and 80% of all

samples are classified correctly within Bonaparte, Clotilde and Tricia. The

results of the same analysis performed on the ATR-FTIR data of the juices

using 20 selected wavenumbers are shown in Figure 4.14. In contrast to the

extracts, all three cultivars are separated from each other and 90% to 100%

of the samples are classified correctly (Table 4.13). Tricia has positive PC 1

scores, while the other two cultivars have negative PC 1 scores. Bonaparte


and Clotilde are again separated from each other along PC 2. Tricia is cor-

related with wavenumbers 1466 cm−1, 1425 cm−1, 1396 cm−1, 1338 cm−1,

1165 cm−1 1146 cm−1, 1115 cm−1, 1090 cm−1 and 1012 cm−1. Bonaparte

and Clotilde are not correlated with any of the selected wavenumbers. This

analysis shows that large amounts of chemical information which is impor-

tant for the classification were lost during the extraction. While the juices

are grouped easily based on their chemical content, this is not possible for

the extracted samples.

Glucose

Malic acid

Glutamate

1425

1115

11011090

10741057

10431012

Bonaparte

0 0

0.2

0.4

0.6

0.8

1.0

PC

2

PC 1

Fructose

Citric acid

1743

1466

144814251396

136713381269

1223

12001165

11461012

993Tricia

Clotilde

-1.0

-0.8

-0.6

-0.4

-0.2

0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Figure 4.15: Correlation loadings plot of the PLS-DA of the tomato juices mea-

sured by EHT and ATR-FTIR (X-expl. (84%, 3%); Y-expl. (56%, 10%)).

To correlate the results of the reference analysis performed on juices to

the ATR-FTIR results, a PLS-DA was performed on both data sets together.

Figure 4.15 shows the correlation loadings plot indicating the cultivars, taste

compounds and wavenumbers. All of the 20 selected wavenumbers, except

for 1743 cm−1, are positively correlated with both glucose and fructose and

132 4.3 Results

negatively correlated with Tricia. This implies that Tricia contains low

contents of glucose and fructose, which is reflected in a low absorbance. As

shown in Figure 4.1, wavenumber 1743 cm−1 is located on the flank of the

large peak at 1722 cm−1. This peak is the result of high absorption of IR

light by C=O bonds in citric and malic acid. In the sugar spectra, there is

no peak at this wavenumber, indicating why there is no correlation between

1743 cm−1 and the two sugars.

4.3.3.3 Quantification with ATR-FTIR

The main PLS2 results of both the extracts and juices are shown in Table

4.14. Despite the fact that exactly the same samples were analyzed us-

ing ATR-FTIR and the reference technique, the calibration and validation

models of the extracts and juices do not show any possibility of ATR-FTIR

to predict the chemical composition of the samples. The correlations be-

tween the compounds present in the tomato samples and the ATR-FTIR

absorbance spectra are low for all analyses. Even though the models built

on the extracted samples report the highest correlations, the RMSECV and

RPD values are not satisfactory. The validation models for citric acid in

both the extracts and juices show negative correlations. The RPD values of

all compounds analyzed both as extracts and juices are lower than 2, indi-

cating that only high and low values can be distinguished. An explanation

for the bad prediction models is found in the fact that the three tomato

cultivars only cover a very small concentration range. The range of glu-

cose present in the juices is the largest and goes from 9.8 g/L to 14.7 g/L.

In a next experiment the range of concentrations of all compounds will be

broadened using a dilution of the samples and a standard addition of two

mixtures to the samples.


Table 4.14: Correlations, RMSECV and RPD values found in the PLS2 models

to predict individual compounds measured by EHT in tomato extracts (mg/g) and

juices (g/L) built on the results of the ATR-FTIR measurements. Cross-validation

was used to validate the model.

Compound Extract Juice

Malic acid R 0.10 0.30

RMSECV 0.34 0.28

RPD 1.0 1.1

Citric acid R -0.31 -0.54

RMSECV 0.67 0.53

RPD 1.0 1.0

Glutamate R 0.71 0.56

RMSECV 0.27 0.33

RPD 1.4 1.2

Glucose R 0.80 0.75

RMSECV 1.25 1.34

RPD 1.7 1.6

Fructose R 0.76 0.64

RMSECV 1.08 1.09

RPD 1.6 1.5

4.3.4 Dilutions and standard additions

The absorbance spectrum of Loredana is shown together with the spectra

of the diluted sample and samples with addition of mixture 1 and mixture

2 in Figure 4.16. The original sample shows a higher absorption than the

diluted sample in the whole spectral region studied. The spectra of the

samples with an addition of mixture 1 and mixture 2 have the same shape.

The inconsistency in the spectra around 1600 cm−1 comes from differences

in the water content of the presented samples.

134 4.3 Results

Table 4.15: Average results of EHT measurements performed on the original

tomato samples (average ± standard deviation, concentrations in g/L).

Cultivar Glucose Fructose Sucrose Citric acid Malic acid

Macarena 8±1 24±4 4±1 4±3 0.5±0.1

Growdena 7±1 25±7 4±1 3±1 0.53±0.08

Tricia 7±2 24±5 5±3 4±2 0.4±0.1

Admiro 6±1 21±5 4±2 5±2 0.5±0.1

Loredana 5±1 21±5 3±3 5±3 0.8±0.9

Cherry tomato 13±2 39±10 8±2 7±2 0.5±0.1

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

1799

1753

1707

1660

1614

1568

1522

1475

1429

1383

1336

1290

1244

1198

1151

1105

1059

1012 966

920

Abs

orba

nce

Wavenumber (cm-1)

DilutionAddition of mix 1Addition of mix 2Original

Figure 4.16: Average ATR-FTIR absorbance spectra of Loredana: diluted sample,

original sample and samples with standard additions.

A PLS2 analysis was performed on the samples analyzed using ATR-

FTIR. The absorbances at 20 selected wavenumbers were used in the anal-

ysis (Table 4.9). The diluted samples, the original samples and the samples


with an addition of mixture 1 and mixture 2 were included in the PLS2

models to increase the range of chemical components. The results of the

prediction models are presented in Table 4.16. Due to the large concentra-

tion range present in the samples, acceptable prediction models are made

with ATR-FTIR. High correlations are observed in the calibration models

of almost all compounds, with values above 90% for glucose, fructose and

malic acid. Despite the high correlations between the measured and the pre-

dicted concentrations, high offsets, RMSEC and RMSECV values are found.

The RPD values of all three compounds are higher than 2, with values of

respectively 3.1, 2.3 and 2.2 for glucose, fructose and malic acid. With a

correlation of 86% the prediction model of citric acid still has a RPD value

of 2.

Table 4.16: PLS2 models to predict individual compounds measured by EHT in

tomato samples built on the results of the ATR-FTIR measurements of the diluted

samples, original samples and samples with a standard addition. Cross-validation

was used to validate the model. The offsets, RMSEC and RMSECV values are

given in g/L.


RMSECV


Validation 0.81 0.28 0.89 0.51 2.2


Validation 0.74 1.68 0.86 1.67 2.0


Validation 0.70 1.71 0.83 2.10 1.7


Validation 0.90 0.82 0.95 1.15 3.1


Validation 0.81 4.29 0.90 4.45 2.3


The multifruit juices, syrups and mixtures, which were analyzed with the

ETSPU, were also analyzed with ATR-FTIR. Figure 4.17 shows the ab-

136 4.3 Results

sorbance spectra of the nine syrups. Large differences are observed in the

absorbance spectra. The absorbance spectrum of lemon syrup differs greatly

from the absorbance spectra of the other syrups. Based on the absorption

spectrum of the lemon syrup is found that this syrup contains high amounts

of C=O bonds, which are present in acids, and low amounts of C-O bonds,

which are mainly present in sugars. Orange syrup and blend-9 fruit syrup

both absorb highly in the spectral area between 1004 cm−1 and 987 cm−1.

The apple syrup absorbs the most IR light of all syrups between 1081 cm−1

and 993 cm−1 which is caused by the absorption of IR light by the C-O

bonds of the sugars present in this syrup.

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

1797

1750

1704

1658

1612

1565

1519

1473

1426

1380

1334

1288

1241

1195

1149

1103

1056

1010 964

918

Abs

orba

nce

Wavenumber (cm-1)

Blend-9 fruit LemonOrange Passion fruitApple Red grapeElderberry CherryStrawberry

Figure 4.17: Average ATR-FTIR absorbance spectra of nine fruit syrups.

The absorbance spectra of elderberry syrup and red grape syrup have a

very similar shape and show some resemblance to the spectra of strawberry

syrup and cherry syrup. Elderberry syrup, however, absorbs more light than

any other syrups around 1581 cm−1.


The differences in the absorbance spectra of the syrups are reflected in

the PCA. The nine syrups and 11 mixtures produced from the individual

syrups (Table 3.4) were analyzed using PCA (Figure 4.18). The full first

derivative spectra were used in the analysis since the differences in chemical

composition make it impossible to select 20 wavenumbers which are repre-

sentative for the absorbance spectrum of all the syrups. Lemon syrup is

separated from the other syrups and mixtures. This separation is caused

by the very different absorbance spectrum of lemon syrup compared to the

other syrups.

0.2

0.3

0.4

PC

2Blend-9 fruit LemonOrange Passion fruitApple Red grapeElderberry CherryStrawberry Mix 1Mix 2 Mix 3Mix 4 Mix 5Mix 6 Mix 7Mix 8 Mix 9Mix 10 Mix 11

-0.2

-0.1

0.0

0.1

-0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4

PC 1

Figure 4.18: Score plot of the PCA of the 9 syrups and 11 mixtures of syrups

measured by ATR-FTIR (X-expl. (52%, 27%)).

This classification is quite different from that of the ETSPU analysis.

Blend-9 fruit syrup and orange syrup are located closely to each other and

mixtures 4, 6, 7 and 8 and the mixture resembling Ace (mixture 1). All

of the mixtures, except for mixture 8, contain high concentrations of both

138 4.3 Results

blend-9 fruit and orange syrup, which explains the close position on the

score plot. Elderberry syrup, red grape syrup, strawberry syrup and cherry

syrup are classified together with four mixtures which are produced out of

these syrups. The grouping of the four syrups is not retrieved when analyz-

ing the samples with the ETSPU, where they are classified into two groups,

surrounding the mixtures that contain these syrups. The apple syrup, fi-

nally, is positioned closely to two mixtures which contain high contents of

apple syrup. After performing the analysis without lemon syrup, similar re-

sults are found (not shown). Syrups with similar composition and mixtures

containing the same syrups are positioned together in the score plot of a

PCA.

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

1797

1750

1704

1658

1612

1565

1519

1473

1426

1380

1334

1288

1241

1195

1149

1103

1056

1010 964

918

Abs

orba

nce

Wavenumber (cm-1)

Ace

Benefits Vitality

Benefits Immunity

Figure 4.19: Average ATR-FTIR absorbance spectra of the three multifruit juices.

The absorbance spectra of the three multifruit juices, Ace, Benefits Vi-

tality and Benefits Immunity, are shown in Figure 4.19. The multifruit juices

absorb less IR light than the syrups they are made of. An explanation for


this is found in the composition of the multifruit juices. All three juices

contain amounts of extra compounds next to the syrups (Table 3.3). The

spectra of Benefits Vitality and Benefits Immunity are very similar, despite

their differences in composition. The multifruit juice Ace absorbs less IR

light than the other two juices due to the lower fruit syrup content and the

presence of water in this juice.

The three mixtures that were made based on information of the fruit

content of the three multifruit juices were analyzed together with the three

multifruit juices (Figure 4.20). The three multifruit juices are clearly sepa-

rated from the three mixtures along PC 1, however, the same trend is visible

in the two groups of samples. More information and absorbance spectra of

the extra components (aloe vera puree, minerals, vitamins) present in the

multifruit juices are necessary to make a complete classification model based

on the fruit content.

-0.20

-0.10

0.00

0.10

0.20

-0.30 -0.20 -0.10 0.00 0.10 0.20 0.30

PC

2

PC 1AceBenefits Vitality

Benefits Immunity

Mix Ace

Mix Benefits Vitality

Mix Benefits Immunity

Figure 4.20: Score plot of the PCA of the three multifruit juices and the mixtures

with the same fruit content measured by ATR-FTIR (X-expl. (76%, 14%)).

140 4.3 Results

The possibility to predict the fruit syrup content in the three multifruit

juices using ATR-FTIR was studied in a PLS2 analysis. The results are

shown in Table 4.17. The PLS2 model gives good results for all nine syrups,

with high slopes and correlations and low offsets and errors. Also, low

RMSEC and RMSECV values are found, especially for lemon, passion fruit,

cherry and strawberry with values lower than 0.10.

Table 4.17: PLS2 model to predict individual syrups in multifruit juices based

on the ATR-FTIR spectra. Cross-validation was used to validate the model. The

offsets, RMSEC and RMSECV values are given in % v/v

Syrup Slope Offset Correlation RMSEC

RMSECV

Blend-9 fruit Calibration 0.99 0.001 0.99 0.11

Validation 0.99 0.02 0.99 0.15

Lemon Calibration 0.99 0.001 0.99 0.01

Validation 0.99 0.001 0.99 0.01

Orange Calibration 0.99 0.01 0.99 0.51

Validation 0.99 0.13 0.99 0.68

Passion fruit Calibration 0.99 0.001 0.99 0.04

Validation 0.99 0.01 0.99 0.05

Apple Calibration 0.99 0.001 0.99 0.22

Validation 0.99 0.03 0.99 0.29

Red grape Calibration 0.99 0.001 0.99 0.20

Validation 0.99 0.06 0.99 0.27

Elderberry Calibration 0.99 0.001 0.99 0.12

Validation 0.99 0.03 0.99 0.16

Cherry Calibration 0.99 0.001 0.99 0.03

Validation 0.99 0.01 0.99 0.04

Strawberry Calibration 0.99 0.001 0.99 0.04

Validation 0.99 0.01 0.99 0.06

In addition to this prediction, the ability of ATR-FTIR to detect small

differences in the syrup content of the multifruit juices is evaluated. Hereto,

different amounts of passion fruit and cherry syrup are added to respectively

Benefits Vitality and Benefits Immunity. Figure 4.21 illustrates the changes


which occur in the absorption spectrum of Benefits Immunity due to the

addition of cherry syrup. The total amount of absorbed light increases

because (i) by adding syrup to the multifruit juice, the samples become

less diluted and thus absorb more; (ii) the syrup is highly concentrated

and, thus, contains large amounts of sugars and acids, causing more light

to be absorbed. A PLS1 analysis was performed on the data to determine

whether additions of syrup in a multifruit juice can be detected using ATR-

FTIR (Table 4.18). Both the calibration and validation model of cherry and

passion fruit syrup have high correlations and low RMSE values. The high

RPD values indicate that both syrups are traceable and can be predicted

excellently. This proves that ATR-FTIR is very accurate to predict small

amounts in a complex matrix.

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1797

1751

1705

1659

1612

1566

1520

1473

1427

1381

1335

1288

1242

1196

1149

1103

1057

1011 964

918

Abs

orba

nce

Wavenumber (cm-1)

10:09:18:27:36:45:5

Figure 4.21: ATR-FTIR absorbance spectra of additions of cherry syrups to

Benefits Immunity. The multifruit juice:syrup ratio is indicated.

142 4.4 Discussion

Table 4.18: Main validation results of PLS1 model using cross-validation to pre-

dict added cherry and passion fruit syrup in two multifruit juices based on the

ATR-FTIR spectra (% v/v).

Syrup Correlation RMSECV RPD

Passion fruit 0.99 0.12 15

Cherry 0.99 0.27 6.4

4.4 Discussion

4.4.1 Classification of samples

The absorbance spectra of the taste compounds that are important for fruit

were collected using ATR-FTIR. Glucose, fructose, sucrose, citric acid and

malic acid were analyzed. Large differences between the absorbance spectra

of the three sugars and the two acids are found. Two vibrations are mainly

responsible for these large differences. C=O stretching vibrations at 1722

cm−1 are present in the spectra of both acids. These vibrations are very

specific for acid recognition based on absorbance spectra. C-O vibrations

between 1100 cm−1 and 950 cm−1, on the other hand, are mainly present in

the spectra of the three sugars. The importance of this vibration was also

found by Back et al. (1984) and De Lene Mirouze et al. (1993) while studying

carbohydrates in aqueous solutions and glucose syrups, respectively. Sucrose

has, due to its specific chemical structure, an absorbance spectrum with large

differences between glucose and fructose. The high absorbances at 997 cm−1

and 923 cm−1 make it possible to recognize the presence of sucrose in spectra

of sugars, which was also described by Irudayaraj and Tewari (2003).

The ability of ATR-FTIR to classify apple and tomato cultivars based on

their taste compounds was studied in a second experiment. Like the ETSPU,

ATR-FTIR is able to discriminate between apples and tomatoes. This seems

very plausible. Nevertheless, similar taste compounds are present in both

fruit. While the ET was only able to discriminate between very different cul-

tivars, ATR-FTIR can classify the apple and tomato cultivars more clearly.

The differences in the absorbance spectra of the five apple cultivars and


four tomato cultivars are related to differences in the chemical composition

of the fruit. High absorbances between 1100 cm−1 and 900 cm−1 are mainly

assigned to C-O stretching vibrations of glucose, fructose and also sucrose

in apple. Cultivars with high absorbances in this region of wavenumbers,

contain a high content of one or more of the studied sugars. The possibility

of ATR-FTIR to correlate the absorbances at specific wavenumbers to chem-

ical compounds is a large advantage of this technique over the ET, which

rather follows a black box approach. In this experiment, the analysis of

the reference measurements was performed on extracted apple and tomato

samples. The ATR-FTIR measurements, on the other hand, were carried

out on juices. This discrepancy was looked at in a next experiment.

Two types of sample preparation were evaluated into detail. The po-

tential of EHT, as a reference technique, and ATR-FTIR to discriminate

between cultivars was used to determine which sample preparation tech-

nique should be used for rapid analysis of taste compounds. The results of

the analysis with EHT are very similar for both the extracts and juices. This

means that no crucial information on the taste compounds gets lost during

the extraction process. When analyzing the same samples with ATR-FTIR,

however, different results are found for both sample preparation techniques.

The absorbance spectra of the extracted samples contain a lot less informa-

tion on the chemical composition than the spectra of the juices. Clearly,

during the extraction process some chemical information is lost. From the

PLS-DA can be concluded that the discrimination of samples using ATR-

FTIR is based on matrix constituents other than just the carbohydrates and

organic acids. To minimize sample preparation time, the analysis with both

the reference technique and ATR-FTIR should be performed on juices. This

was also stated by Irudayaraj and Tewari (2003). Garrigues et al. (2000)

found that a minimal sample preparation resulted in the most accurate re-

sults, since extensive sample preparation requires perfect repeatability and

can lead to a large variability in absorbance.

144 4.4 Discussion

4.4.2 Quantification of taste compounds

A dilution series and mixtures of pure taste compounds were analyzed us-

ing ATR-FTIR. The results showed there is a linear relation between the

concentration of a compound and its absorption spectrum and that spectra

can be added to each other as a result of Beer’s Law (Chalmers and Grif-

fiths, 2002). Prediction models based on the individual compounds were

made using different selection processes for the variables. A selection of

wavenumbers was also proposed by Kelly and Downey (2005), Irudayaraj

and Tewari (2003), Garrigues et al. (2000) and Vonach et al. (1998) to re-

duce the size of the data matrix and shorten analysis time. The PLS results

showed that ATR-FTIR is able to predict the concentration of all studied

compounds correctly using only two selected wavenumbers. Garrigues et al.

(2000), however, found that the use of a selected wavenumber is not enough

to predict the content of sucrose in a complex mixture due to interferences.

It seems necessary to look at the absorbances at more wavenumbers in or-

der to verify changes in the sugar bands. This was also found in the results

presented in this thesis. By selecting the minima and/or maxima of the

first derivative absorbance spectra, the main vibrations of the studied com-

pounds are used for further data analysis. The PLS models indicated that

this is the most optimal selection procedure. From this experiment can be

concluded that ATR-FTIR is able to predict taste compounds in mixtures

if their concentration is above the determined LOD values.

In a second experiment, the main taste compounds of apple and tomato

were predicted using ATR-FTIR. Despite the fact that the PLS2 models are

better than those described in Chapter 3, the models give results which are

not satisfactory. There are two possible explanations for the bad results.

First, as mentioned in Chapter 3, the reference measurements performed on

these samples were performed using HPLC and, thus, an extensive sample

pretreatment was needed for the reference analysis. The measurements with

ATR-FTIR and HPLC were performed on samples which had undergone

different samples treatments. Second, since the LOD’s indicate that all

compounds could be detected in the samples, the concentration ranges of


the taste compounds and the tomato matrix would be expected to influence

the predictions.

In a third experiment two sample preparation techniques were compared.

Both extracts and juices were used for the reference analysis and the ATR-

FTIR measurements. Since the PLS2 analysis shows similar results for the

extracts and juices, samples could be analyzed by EHT and ATR-FTIR

with a minimal sample preparation. Irudayaraj and Tewari (2003) reported

that this indicates that ATR-FTIR has potential as a routine procedure for

the quantification of multiple constituents without any sample preparation

in quality control. In this experiment, however, the prediction models for

all taste compounds in the juices are poor, with low correlations and RPD

values. This indicates the importance of the concentration ranges and the

matrix. Schindler et al. (1998a) observed the same problem in the analysis

of wine samples. His prediction models for tartaric acid and acetic acid,

which are present in small concentration ranges, showed low correlations

between the measured and predicted values in model solutions.

The ability of ATR-FTIR to predict taste compounds was studied further

using standard additions of taste compounds. In a fourth experiment the

range of concentrations of all compounds was broadened using a dilution

of the samples and a standard addition of two mixtures to the samples.

The PLS2 models resulting from the additions to the tomato samples give

far better results than the previous experiments confirming the importance

of the matrix. RPD values close to and higher than 2 are found for all

compounds, indicating that all compounds can be predicted (Saeys et al.,

2005).

To improve the quantification rate of taste compounds using ATR-FTIR,

without adding an extensive separation of the compound of interest, sev-

eral statistical techniques could come to aid. These techniques deal with

the problem of finding a representative calibration set for the prediction

model. A representative calibration set is not so easy to collect, because

not only the expected variation in the compounds of interest should be in-

cluded, but also the variation of the other contributing factors, interferents,

146 4.4 Discussion

and their correlations with each other and with the compounds of interest.

Many researchers have investigated methodologies to increase the robust-

ness of prediction models against changes in the interferent structure. A

first approach aims to remove the influence of these interferents by means of

a pre-processing of the data, like extended multiplicative signal correction

(EMSC), pre-whitening and physics-based multiplicative scatter correction

(MSC). A second approach tries to expand the calibration set in order to in-

clude all variation. Recently, a new class of multivariate calibration methods

called augmented classical least squares (ACLS) has been proposed which

is an extension of the classical least squares model (CLS) to handle cases

where not all compounds contributing to the absorbance signal are explicitly

included in the calibration models (Martens and Stark, 1991; Martens and

Naes, 1998; Saeys et al., 2008).


The potential of ATR-FTIR to be used as a tool for quality control was eval-

uated using multifruit juices. Errors in the production process of all food

products should be detected rapidly. The classification of the syrups and

mixtures with PCA is clearly based on the fruit syrup content. Fruits which

are closely related are classified together and mixtures of syrups with sim-

ilar compositions are grouped separately from mixtures with different con-

tents. Clearly, ATR-FTIR can detect differences in the fruit syrup content of

juices. Since similar results were found in the previous chapter dealing with

ET technology, it can be concluded that both ATR-FTIR and the ETSPU

have potential for applications in food quality control and food adulteration.

It is, however, not completely possible to group the three multifruit juices

together with the mixtures which have the same syrup composition. Since

the fruit content is the same in both the multifruit juices and the mixtures

related with it, the separation is probably caused by the presence of other

compounds, like the vitamins, aloe vera puree, etc. As showed in the PLS

models based on the analysis of the syrups and fruit juices, ATR-FTIR is,

like the ETSPU, sensitive to small differences in the chemical composition


of a sample. Perfect calibration and validation models were constructed for

all syrups, even those which are only present in very small amounts. The

addition of a syrup to the multifruit juice, furthermore, showed that this is

immediately detected using ATR-FTIR. The addition of extra compounds

like vitamins or aloe vera puree would thus immediately cause large differ-

ences in the absorption spectra of the sample. Detailed information on the

extra compounds could, however, not be given by the manufacturer, due to

the company’s policy on product secrecy. With these results and those found

in literature (Innawong et al., 2004; Lachenmeier, 2007), FTIR seems useful

for quality control and monitoring processes in the food industry because of

its possibility to simultaneously quantify essential compounds and classify

samples. Its speed, good performance and easy use are extra advantages of

FTIR.

4.5 Conclusions

In this chapter, the potential of ATR-FTIR to classify fruit samples and

to quantify their most important taste compounds was studied in several

experiments with a wide variety of samples. ATR-FTIR proved to be a

good tool for recognition, classification, determination and quality control

of fruit juices with different chemical compositions. The short measurement

time and easy use indicate the possibilities of the system as an instrument

in the food industry.

In a first part, the ability of the system to classify samples based on their

taste compounds was studied. Using ATR-FTIR different solutions of glu-

cose, fructose, sucrose, citric acid and malic acid were analyzed to determine

the important peaks in the absorbance spectra. C=O stretching vibrations

and C-O vibrations are related to organic acids and carbohydrates, respec-

tively, which are important taste compounds in fruit. Using the absorbances

at selected wavenumbers, apple and tomato samples were clustered in a sec-

ond experiment. ATR-FTIR proved to be a useful tool for the classification

of cultivars based on their absorbance spectra and, thus, chemical composi-

148 4.5 Conclusions

tion. The influence of sample preparation to classify tomato samples with

ATR-FTIR was studied. Each sample was prepared both as an extract and

a juice and analyzed using a reference technique and ATR-FTIR. Based

on their ATR-FTIR absorbance spectra, the tomatoes were separated less

good as an extract. The absorbance spectra of the extracts showed that a

large amount of chemical information is lost during the extraction process

compared to the juices, which emphasizes the matrix effect. The possibility

to use juices for both reference and ATR-FTIR analysis is a great advan-

tage, which makes the correlation between the reference measurements and

ATR-FTIR analysis more clear and reliable. Less sample preparation also

enhances the possibilities to use ATR-FTIR as a rapid technique, with a

measurement time of only 30 seconds, in high throughput analysis. The

classification results presented are better than those of the ET.

Second, the ability of ATR-FTIR to quantify taste compounds was eval-

uated. Dilution series of individual carbohydrates and organic acids and

mixtures of these compounds show that it is possible to determine their ex-

act concentration in aqueous solutions using ATR-FTIR based on selected

wavenumbers. The use of the minima and maxima of the first derivative

spectra, being the inflection points of the peaks of the absorbance spectra,

gives the most accurate prediction models. The possibility of ATR-FTIR

to determine taste compounds in actual fruit samples was studied using

apple and tomato samples. The predictions, however, were poor. Using

standard additions and dilutions of the samples the concentration ranges

of the taste compounds were enlarged, which resulted in good prediction

models. This indicates that the matrix effects are considerable. Future re-

search should focus on chemometrical methods to separate matrix effects

from useful chemical information.

Finally, the potential of ATR-FTIR as a tool for rapid quality control

was studied. Using IR spectroscopy it is possible to classify the multifruit

juices according to their composition. However, mixtures prepared using

syrups are classified as different from the multifruit juices, possibly due to

the presence of extra compounds in the multifruit juices. Information on

the extra compounds is necessary to make a good classification model based


on the fruit content. The concentration of individual syrups, however, can

be predicted very accurately in the multifruit juices using ATR-FTIR. Us-

ing additions of syrups to the multifruit juices, this technique has proved

to be highly suitable for the detection of small differences between sam-

ples. ATR-FTIR is an important tool for quality control because of its good

performance, easy use and detection speed.

150 4.5 Conclusions

Chapter 5

Sequential injection

ATR-FTIR

5.1 Introduction

In the last decades there has been a shift towards different flow through

systems to speed up and facilitate measurements. Flow injection analysis

(FIA) was first applied by Ruzicka and Hansen in Denmark in 1974 (Ruzicka

and Hansen, 1975). Since then the technique has been further developed and

coupled to spectroscopic and electrochemical detection (Lenehan et al., 2002;

Perez-Olmos et al., 2005). The introduction of sequential injection analysis

(SIA) in the early 1990s broadened the possibilities of flow analysis. SIA

is a technique that has great potential for on-line measurements due to

the simplicity and convenience with which sample manipulations can be

automated (Ruzicka and Marshall, 1990; Economou, 2005).

The synergistic combination between flow through systems and FTIR

developed in the last years provides (i) a simple, fast and reproducible way

for loading and cleaning of the IR flow cells, (ii) repeatability and accuracy,

(iii) an important saving in terms of reagent and time of analysis, (iv) con-

tinuous monitoring of the spectral baseline and accurate determination of

the absorption band maxima, and (v) simultaneous determination of a series

151

152 5.1 Introduction

of compounds in the same sample. The main advantages of IR detection in

flow analysis systems include ease of operation, real-time detection and low

maintenance (Schindler and Lendl, 1999; Gallignani and Brunetto, 2004).

Flow analysis IR techniques are described as powerful analytical techniques

which could provide simple and adequate solutions for the analysis of a lot

of complex and real samples (Lendl and Schindler, 1999). The technique

has been applied to the determination of diverse analytes in fruit juices by

Rosenberg and Kellner (1994) and Kellner et al. (1997).

The potential of ATR-FTIR for food analysis has been demonstrated in

the previous chapter and in many publications. Most described applications,

however, deal with the major disadvantage of extensive sample manipulation

and cleaning of the ATR sampling compartment, which greatly reduces the

sampling rate. To address this problem, in this chapter, the potential of SIA

in combination with ATR-FTIR will be studied for the analysis of Belgian

tomato samples, with a large emphasis on the development and optimization

of the system.

The objective of this chapter is to develop a high throughput technique

using a flow through system based on SIA and FTIR to analyze taste com-

ponents of fruit samples in large scale experiments. Since most of the pub-

lications on flow analysis IR techniques do not deal with the development

and optimization of the measurement technique itself, this will be studied

intensively. Following topics will be studied in this chapter:

� The flow injection system will be optimized for ATR-FTIR measure-

ments of fruit samples. It should be noted that although the system is

specifically optimized for tomato samples, it is applicable for the anal-

ysis of other fruit or vegetable samples containing the same sugars and

acids as tomato.

� The developed SIA-ATR-FTIR technique will be used to analyze real

tomato samples. The potential of the system as an optical tongue to

classify tomato cultivars according to their taste profile will be evalu-

ated and compared to that of the two ET’s which were investigated in

Chapter 3.

Sequential injection ATR-FTIR 153

� The ability of SIA-ATR-FTIR to quantify the organic acid and carbo-

hydrate content of tomato samples will also be evaluated and compared

to the results of an enzyme based reference technique and two ET’s.

The ability of the developed SIA-ATR-FTIR system to predict taste

as scored by a sensory panel will be discussed in Chapter 6.


and methods which were used are described. The results of experiments with

mixtures and tomatoes are presented in Section 5.3. First, the development

and optimization of the flow through system are described in detail. Second,

the results of the classification of the tomato cultivars are presented. Finally,

calibration models to quantify individual taste compounds are constructed.

In Section 5.4 the results are discussed and compared to literature findings.

Concluding remarks are formulated in Section 5.5. The results presented

in this chapter have been published in Beullens et al. (2006b, 2007c). Two

publications on the optimization of the SIA system and the correlation with

sensory panels have been submitted (Beullens et al., 2008c) and (Beullens

et al., 2008a).

5.2 Materials and Methods

5.2.1 Samples

5.2.1.1 Mixtures

Four mixtures of three sugars (glucose, fructose and sucrose) and four acids

(citric acid, malic acid, quinic acid and tartaric acid) were prepared in tripli-

cate for analysis using the flow analysis ATR-FTIR system. The composition

of the mixtures is shown in Table 5.1a. Stock solutions of the mixtures were

prepared and stored at −80 ◦C until measurement. In an extra experiment

to complete the optimization of the flow injection ATR-FTIR system four

different mixtures of the same sugars and acids were prepared in triplicate.

The composition of the four mixtures is shown in Table 5.1b. The concentra-


Table 5.1: Composition of four mixtures used in the optimization of the flow

system coupled to ATR-FTIR. Concentrations are given in g/L.

Glucose Fructose Sucrose Citric Malic Tartaric Quinic

acid acid acid acid

a.

Mixture 1 20 20 5 10 30 10 15

Mixture 2 40 40 20 5 15 15 10

Mixture 3 10 10 30 30 10 5 5

Mixture 4 30 30 10 20 5 2 2

b.

Mixture 1 20 20 5 10 20 5 5

Mixture 2 40 40 20 5 15 10 10

Mixture 3 10 10 30 30 10 2 7

Mixture 4 30 30 10 20 5 7 2

tions of all acids were smaller than in the previous part of the optimization

so that the mixtures resembled more to real fruit samples. Stock solutions

of the mixtures were prepared and also stored at −80 ◦C until measurement.

5.2.1.2 Tomato samples

Six tomato cultivars (Lycopersicon esculentum Mill.) were selected based

on their difference in taste determined by a sensory panel, which is mainly

defined by differences in sweetness and sourness, to assure a broad range

in acid and sugar content. The selected cultivars are: Admiro, Macarena,

Sunstream, Amoroso, Tricia and Clotilde (Table 3.2). The fruit were ob-

tained at the fruit- and vegetable Auctions of Mechelen and Hoogstraten

in Belgium. All tomatoes were picked at ripeness stage 5 (light red class)

(USDA, 1975). The fruit were stored during one day at ambient atmosphere

(18 ◦C and 80% relative humidity). The day after purchase 10 L of tomato

juice was collected in one recipient. Subsequently, the juice was divided over

several falcon tubes and frozen in liquid nitrogen. The samples were stored

at −80 ◦C until analysis with SIA-ATR-FTIR, two types of ET’s (Chapter

3), EHT and a trained sensory panel (Chapter 6).


5.2.2 Measurement techniques

5.2.2.1 ATR-FTIR

The ATR-FTIR measurements were performed on a Bruker Tensor 27 spec-

trometer (Bruker, Karlsruhe, Germany) equipped with a mid-IR source and

a MCT detector. The sampling station contained a flow-through horizon-

tal ATR accessory with multiple reflections (PIKE Technologies, Madison,

USA). A closed AMTIR crystal with a channel for sample containment (0.5

mL) was used. Background spectra were collected between every measure-

ment using distilled water. The number of co-added scans and the resolution

were optimized in the flow system. Single beam spectra in the range of 1800

cm−1 to 900 cm−1 were obtained and corrected against the background to

present the spectra in absorbance units. OPUS software version 5.5 (Bruker,

Karlsruhe, Germany) was used to operate the FTIR spectrometer and col-

lect all the data. During ATR-FTIR measurement the samples were placed

in a temperature controlled water bath (Julabo TW8, VWR, Belgium).

5.2.2.2 Sequential injection analyzer

The flow through system consisted of a pump and valve system. The milli-

GAT pump combined with a Microlynx-4 micro-electric controller (Global

FIA Inc., Fox Island, USA) was chosen for its large bi-directional pulseless

flow range from 60 nL/min up to 50 mL/min. A large advantage of the pump

is the redundancy of refill cycles or syringe changes unlike traditionally used

syringe pumps. The stepper motor of the pump is fully computer control-

lable. The milliGAT pump was connected through teflon tubing (1/16”OD

0.030”ID, Alltech Associates Inc., Deerfield, USA) to the ATR cell and a 10-

port injection valve (Cheminert, C22Z-2180D, Valco Instruments Co. Inc.,

Houston, USA) with two valve positions. The valve position, flow rate and

direction were controlled by a computer program written in Labview 8.0 (Na-

tional Instruments Co., Austin, USA). A general schematic flow diagram of

the sequential injection analyzer is shown in Figure 5.1.


Valve

Sample

Distilled water

Pump Detector

Waste

Figure 5.1: Schematic flow diagram of the developed sequential injection analyser.


An enzymatic high throughput method, EHT, was used as a reference tech-

nique to evaluate the sugar and acid content of the tomato juices. Details

on the sample preparation and operational settings are given in Chapter 3.

5.2.3 Optimization design

The SIA-ATR-FTIR system was optimized with respect to four parameters:

� resolution

� number of co-added scans

� flow rate

� temperature of the sample.


The first three instrumental parameters determine the measurement accu-

racy, the sample volume and the measurement time, while the fourth one

might influence the repeatability of the measurements as influenced by en-

vironmental factors. A central composite design (CCD) was used for the

optimization of the four factors. In a circumscribed CCD the star points

are at a distance α from the center point based on the properties desired

for the design. The star points are extremes for the low and high settings of

all factors (Figure 5.2). This type of design has a hyperspherical symmetry

and requires five levels for each factor (NIST/SEMATECH, 2007).

Facto

r 3

Figure 5.2: Schematic figure of circumscribed central composite design for three

factors.

An overview of the levels per factor is given in Table 5.2. The number of

co-added scans was expressed as a power of two to obtain equidistant levels

for this factor. Compared to a full factorial design, the number of design

points in the CCD was reduced from 625 to 30. For each combination


Table 5.2: Four factors to be optimized in the SIA-ATR-FTIR system and the

levels used in the CCD.

Factor Level 1 Level 2 Level 3 Level 4 Level 5

Resolution (cm−1) 1 2 4 8 16

Co-added scans 16 32 64 128 256

Flow rate (µl/sec) 0 15 30 45 60

Temperature (◦C) 10 15 20 25 30

of design factors in the CCD the whole set of mixtures was measured in

triplicate. Next, joint calibration models were calculated for all compounds

in the mixtures using partial least squares regression (PLS2). The root

mean square error of cross-validation (RMSECV) and ratio of prediction

to deviation (RPD) values were used to assess the model performance for

each component individually. Response surfaces of these two values per

compound were constructed as a function of the four factors and their first

order interactions. Optimal factor levels were defined at the minima in the

response surfaces of the RMSECV values and the maxima in the response

surfaces of the RPD values. When the RPD value of a model is equal to

or higher than 2 it can be accepted as a good prediction model. Since

the optimal set of design factors should be representative for all compounds

jointly, also an average RPD value was calculated over all sugars and organic

acids in the mixtures, and used for optimization. The CCD calculations and

the corresponding data analysis were performed in SAS version 9.1 (SAS

Institute Inc., Cary, USA).


The SIA-ATR-FTIR data were preprocessed before the statistical analysis

by taking the first derivative of the absorption spectra (Savitsky-Golay algo-

rithm, second order polynomial with 5 points at each side). The wavenum-

bers at which a local minimum or maximum occurred in the derivative spec-

trum were selected. The first derivative absorption spectra at these selected

wavenumbers were used for further data analysis.


Multivariate data analysis was applied for quantitative and qualitative

analysis. Partial least squares discriminant analysis (PLS-DA) was per-

formed, as a supervised technique, to cluster the data according to their

chemical composition. The analysis was performed on the covariance ma-

trix. The EHT results were used as a reference to which the SIA-ATR-FTIR

results were compared. Partial least squares regression (PLS2), using cross-

validation, was performed to study the predictive performance of SIA-ATR-

FTIR. The concentration of two sugars, glucose and fructose, and three

acids, citric acid, malic acid and glutamate, were predicted in the tomato

samples using PLS2. The data analysis was carried out using two software

packages: The Unscrambler version 9.0 (CAMO Technologies Inc., Oslo,

Norway) and SAS version 9.1 (SAS Institute Inc., Cary, USA).

5.3 Results

5.3.1 Optimization

For every design point in the CCD design a PLS2 calibration model was

built. The calibration performance characteristics were calculated for each

of the studied sugars and acids. For each compound an RMSECV response

surface was constructed as a function of the design factors. The optimal set

of operational conditions - the minimum in the response surface - for each

individual compound is depicted in Table 5.3. Based on the levels in the

design, a resolution of 16 cm−1 is optimal for all compounds but malic acid.

256 co-added scans result in a significantly improved prediction performance

for sucrose, citric acid, tartaric acid and quinic acid. A flow of 0 µL/sec

(stopped flow) and a temperature of 10 ◦C is optimal in case of sucrose.

However, the value for the flow and temperature do not significantly affect

the model performance for the other compounds. Although the resolution,

the number of co-added scans and the flow rate all show optimal values

at one of their extreme values, it was decided to use these values together

with a stopped-flow as optimal conditions to minimize RMSECV values in

the PLS2 models. Similar results were found in the optimization of the

160 5.3 Results

RPD values. The maximum RPD values were obtained for a resolution of

16 cm−1, 256 co-added scans, a stopped flow and a sample temperature of

10 ◦C.

Table 5.3: Optimal conditions per compound based on RMSECV results. (NS:

not significant).

Compound Resolution Co-added scans Flow rate Temperature

(cm−1) (µl/sec) (◦C)

Glucose 16 NS NS NS

Fructose 16 NS NS NS

Sucrose 16 256 0 10

Citric acid 16 256 NS NS

Malic acid NS NS NS NS

Tartaric acid 16 256 NS NS

Quinic acid 16 256 NS NS

RPD

RPD

3.60

6

2

6.6

3.6

3012

0

60

4.2

7.8

Figure 5.3: Optimization of resolution, number of scans, flow and temperature

using average RPD values.


Since RPD values are dimensionless they are easily averaged out over all

seven compounds, resulting in one RPD value for the whole PLS2 model.

The important design factors were a resolution of 16 cm−1, 256 co-added

scans and a temperature of 25 ◦C (Figure 5.3). For the factor temperature,

these results slightly differ from the previous analysis using RMSECV and

individual RPD values. Hence, an extra experiment was performed to find

the optimal level of temperature.

5

6

7

8

9

10

PD

0

1

2

3

4

5

10 20 30 40 50

RP

Temperature (°C)

Figure 5.4: Optimization of the temperature using average RPD values and op-

timal settings for resolution, number of co-added scans and flow rate.

In this second experiment the resolution, number of co-added scans and

flow rate were taken as optimal, respectively 16 cm−1, 256 co-added scans

and 0 µl/sec. PLS2 calibration curves were made for four mixtures of sugars

and acids (Table 5.1b) at five different temperatures: 10 ◦C, 20 ◦C, 30 ◦C,

40 ◦C and 50 ◦C. The overall RPD value was used to find the optimal tem-

perature. No significant differences were found between the five RPD values

162 5.3 Results

(Figure 5.4). Temperature plays no significant role in the flow injection

ATR-FTIR system to analyze sugars and organic acids. It was chosen to

carry out all measurements at room temperature (± 20 ◦C).

5.3.2 Data exploration of tomato samples

0.00

0.02

0.04

0.06

0.08

0.10

0.12

1790

1743

1697

1651

1605

1558

1512

1466

1419

1373

1327

1281

1234

1188

1142

1095

1049

1003 957

910

Abs

orba

nce

Wavenumber (cm-1)


Figure 5.5: Average ATR-FTIR absorbance spectra of six tomato cultivars.

Figure 5.5 and 5.6 respectively show the absorbance spectrum and first

derivative of the absorbance spectrum of the six tomato cultivars. Large

differences between the tomato cultivars are visible in the absorbance spec-

tra. Amoroso displays a higher absorbance at almost all wavenumbers, es-

pecially in the spectral region between 1140 cm−1 and 1000 cm−1. At these

wavenumbers absorption of light occurs due to strong stretching vibrations

of the C-O bonds present in sugars. Amoroso is known for its sweet and sour

taste; it is a very tasty fruit. The results of the reference measurements (Ta-


ble 3.2) showed that Amoroso contains high concentrations of two sugars,

glucose and fructose. Admiro, however, absorbs more light than Amoroso

and the four other cultivars between 1720 cm−1 and 1620 cm−1. This area

in the spectrum is related to the absorbance light by the C=O stretching

bonds of organic acids. This corresponds to the fact that Amoroso contains

a low concentration of malic acid. Tricia absorbs less IR light than the

other five cultivars at all wavenumbers. Table 3.2 showed that this cultivar

does not contain high concentrations of any of the studied sugars and acids.

Tricia is known as a tomato with a weak taste. The information on the

concentrations of taste compounds in the six cultivars as measured using a

reference technique can thus be reflected in the amount of absorbed IR light.

-0.010

-0.008

-0.006

-0.004

-0.002

0.000

0.002

0.004

0.006

0.008

0.010

1790

1743

1697

1651

1605

1558

1512

1466

1419

1373

1327

1281

1234

1188

1142

1095

1049

1003 957

910

Firs

t der

ivat

ive

abso

rban

ce

Wavenumber (cm-1)


Figure 5.6: Average first derivative of ATR-FTIR absorbance spectra of six

tomato cultivars.

Based on the first derivative absorbance spectra following wavenumbers

were selected for further data analysis: 1751 cm−1, 1689 cm−1, 1612 cm−1,

164 5.3 Results

1542 cm−1, 1450 cm−1, 1373 cm−1, 1349 cm−1, 1326 cm−1, 1265 cm−1,

1218 cm−1, 1103 cm−1 and 1018 cm−1. Despite the different settings of the

resolution and number of scans, most of these wavenumbers are close to the

ones selected in previous experiments with ATR-FTIR (Table 4.9).

5.3.3 Classification of tomato cultivars

The results of the PLS-DA performed on the data of the reference measure-

ments are shown in Figure 5.7. Separation between cultivars based on their

sugar and acid content is clearly achieved using EHT. Almost all separa-

tion occurs along the axis of the first PC. The variation explained by the

second PC is due to the technique used in the analysis. Amoroso, which is

separated from the other cultivars, is highly positively correlated with glu-

tamate, glucose and fructose. Macarena, Sunstream, Tricia and Clotilde are

not correlated to any of the sugars or acids which were measured. Admiro

shows a correlation with malic acid.

Figure 5.8 shows the results of the PLS-DA performed on the absorbances

at the 20 selected wavenumbers of the SIA-ATR-FTIR measurements. The

classification along the axis of the first PC is related to the sugar content

of the samples. Amoroso, which is classified at the negative end of the axis,

contains the highest concentrations of sugars. Tricia is classified at the pos-

itive end of the axis. This cultivar contains the lowest concentrations of

glucose and fructose. Sunstream, Macarena and Clotilde all have a sugar

content which is in between that of Amoroso and Tricia. The first PC can

thus be seen as a sugar axis. 1612 cm−1, 1450 cm−1, 1373 cm−1, 1349 cm−1,

1326 cm−1, 1218 cm−1, 1103 cm−1 and 1018 cm−1 show a correlation of al-

most 100% with the first PC. These wavenumbers are part of the spectral

area in which absorption of IR light is mainly caused by C-O vibrations

in sugars. Admiro is separated from the other cultivars along the axis of

the second PC. The classification table (Table 5.4) also shows that all of

the samples are classified within the correct cultivar using SIA-ATR-FTIR.

The findings of the PLS-DA are in accordance with those from the reference

technique, EHT.


-2

-1

0

1

2

-14 -12 -10 -8 -6 -4 -2 0 2 4 6 8

PC

2

PC 1


A

Citric acid

Malic acid

Admiro

Macarena

Sunstream0.2

0.4

0.6

0.8

1.0

PC

2

PC 1GlucoseFructose

GlutamateAmoroso

Tricia

Clotilde

-1.0

-0.8

-0.6

-0.4

-0.2

0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

PC 1

B


tomato samples measured by EHT (X-expl. (98%, 1%); Y-expl. (20%, 16%)).

166 5.3 Results

0 0002

0.0004

0.0006

0.0008

PC

2AdmiroMacarenaSunstreamAmorosoTriciaClotilde

-0.0004

-0.0002

0.0000

0.0002

-0.006 -0.004 -0.002 0.000 0.002 0.004 0.006

PC 1

A

1751

16121450

1373

1265

12181018

Admiro

Sunstream

0 0

0.2

0.4

0.6

0.8

1.0

PC

2

PC 1

1689

15421349

132612181103

MacarenaAmoroso

Tricia

Clotilde

-1.0

-0.8

-0.6

-0.4

-0.2

0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

B


tomato sample measured by SIA-ATR-FTIR (X-expl. (98%, 1%); Y-expl. (19%,

16%)).



and SIA-ATR-FTIR measurements. The percentage of correct classified samples is

shown (%).

Cultivar EHT SIA-ATR-FTIR

Admiro 80 100

Macarena 100 100

Sunstream 100 100

Amoroso 100 100

Tricia 100 100

Clotilde 100 100


A PLS2 analysis was performed to predict the sugar and acid concentration

in the six tomato cultivars based on their SIA-ATR-FTIR absorption spec-

tra. The results of the analysis are shown in Table 5.5. Four PC’s were used

to find these results. The correlations between the measured and predicted

concentrations of glucose and fructose are high for both the calibration and

the validation models. The offsets of the validation models and RMSECV

values are relatively high, however, high RPD values of 5.0 and 4.1 can

be found for respectively glucose and fructose. The prediction of the acid

concentrations shows different results. The correlations of both calibration

and validation models of citric acid and glutamate are a bit lower, but the

RMSECV values are sufficiently low and the RPD values are higher than 2,

with values of respectively 2.2 and 2.5. The correlation between the mea-

sured and predicted malic acid concentration reaches a value of 0.76. The

RMSECV values of malic acid are low and the RPD value of the prediction

model is 1.6. The difference in correlation between measured and predicted

concentrations of fructose and glucose, the two sugars, on one hand and

citric acid and glutamate on the other hand are notable. This might be

explained by the concentration range of the individual compounds which is

present in the samples. Glucose and fructose respectively have a range from

10 g/L to 24 g/L and 11 g/L to 24 g/L (Table 3.12). The concentration

ranges of citric acid and glutamate are respectively 2.5 g/L to 6.1 g/L and

168 5.4 Discussion

0.6 g/L to 2.9 g/L. Despite these differences, all four prediction models have

an RPD value higher than 2, which indicates that all models are accurate

to predict that specific component. The correlation between the measured

and predicted malic acid concentration reaches a values of 0.76 in the val-

idation model. The range of concentration of malic acid in the samples is

very small, from 0.3 g/L to 0.9 g/L. With this very small range in tomato

it is not possible to predict the concentration of malic acid accurately using

SIA-ATR-FTIR.

Table 5.5: PLS2 models based on the SIA-ATR-FTIR measurements of the tomato

samples. Cross-validation was used to validate the model. The offsets, RMSEC and

RMSECV values are given in g/L.


RMSECV


Validation 0.94 1.06 0.98 0.93 5.0


Validation 0.91 1.46 0.96 1.09 4.1


Validation 0.83 0.73 0.90 0.49 2.2


Validation 0.60 0.22 0.76 0.12 1.6


Validation 0.81 0.25 0.90 0.29 2.5

5.4 Discussion

5.4.1 Optimization

A SIA-ATR-FTIR system was developed and optimized for the analysis

of fruit samples using mixtures of their taste compounds. The optimal

settings, found through studying the average RPD value of the prediction

models of seven taste compounds, are a resolution of 16 cm−1, 256 co-

added scans and a stopped-flow. Since a stopped-flow is used, the sample


volume is minimized and the measurements can be compared to those of

the static system. However, a different resolution and number of co-added

scans is preferred in this SIA system. This indicates that the measurements

performed and discussed in Chapter 4 may give better results when using

a resolution of 16 cm−1 and 256 co-added scans instead of a resolution of 4

cm−1 and 128 scans. It must be stressed that a resolution of 16 cm−1 could

cause a loss in chemical information and therefor is not preferred in FTIR

spectroscopy. The results of the optimization, however, indicate an optimal

quantification of the compounds of interest at this resolution. Long-term

response instability of FTIR spectrometers have been observed in many

studies (Griffiths and de Haseth, 2007). As sample temperature changes,

the density changes and corresponding changes in refractive index occur. In

addition, the width of the absorption bands increases with temperature and

because their area is relatively constant, peak height decreases (Chalmers

and Griffiths, 2002). In this thesis, however, temperature was found not

to have a significant influence on the measurements. The measurement of a

background spectrum immediately before each sample and the measurement

time of less than one minute per sample, make the influence of temperature

insignificant. MacBride et al. (1997) concluded from their experiments that

changes in room temperature should still be minimized, since they can affect

spectrometer stability directly.

5.4.2 Classification of tomato cultivars

The ability of the system to classify tomato cultivars based on their taste

profile was studied. Using the SIA-ATR-FTIR system, tomato cultivars

can be discriminated based on their absorbance spectra and, thus, chemical

composition. The absorbance spectra registered in this experiment show

less noise than the ones shown in Chapter 4. This is the result of the

differences in resolution and number of co-added scans between the dynamic

and static system, with values that are respectively 16 cm−1 and 4 cm−1

and 256 co-added scans and 128 co-added scans. The main vibrations on

which the classification is based are again situated in the regions with C-

170 5.4 Discussion

O stretching vibrations of carbohydrates and C=O vibrations of organic

acids. SIA-ATR-FTIR is a good technique to classify samples based on

their taste compounds. Comparing the technique to ET technology, shows

that both the ETSPU and the ASTREE ET cannot discriminate between

the samples as clear as SIA-ATR-FTIR does. Both ET’s work as black

box systems, making it more difficult to relate the classification directly to

chemical compounds. A direct correlation is possible in ATR-FTIR and

SIA-ATR-FTIR due to the specific vibrations of chemical compounds.


The good classification based on the taste compounds was also reflected in

the possibility of the system to quantify the individual compounds. The

prediction models of glucose and fructose are very accurate, indicating that

both compounds can be predicted in tomato juice using SIA-ATR-FTIR.

The reason for this is the high content of sugars in the samples. Schindler

et al. (1998b) stressed the importance of the characteristic absorbance spec-

tra of sugars to identify them individually in complex samples. The models

predicting citric acid and glutamate show a possibility of the flow through

system coupled to ATR-FTIR to determine the content of both acids in fruit

samples. Malic acid cannot be predicted accurately due to small concentra-

tion ranges in tomato. This same problem was encountered by Schindler

et al. (1998a) while making prediction models for taste compounds in wine.

The models for tartaric acid and acetic acid, which are present in small

concentration ranges, showed low correlations between the measured and

predicted values in model solutions of the taste compounds. Using standard

additions of malic acid or fruit with a higher content and larger differences

in malic acid, the ability of the system to predict this compound could be

studied in future experiments. SIA-ATR-FTIR has proved to be a better

technique to determine the composition of fruit than both ET’s. This again

can be related to the very specific absorbance spectra of the individual com-

pounds and the cross-sensitivity of the sensors in the ET’s making that one

single sensor cannot predict the content of the individual taste compounds


(Legin et al., 1997). The results found in this experiment are better than in

the previous chapter. As mentioned before, this is due to the differences in

settings of the instrument and the wider range of sugars and acids present

in the samples used in the experiment reported in this chapter.

5.5 Conclusions

The possibility of SIA-ATR-FTIR to classify food samples and quantify their

taste compounds was studied in this chapter.

A flow through system was developed and optimized to enable and fa-

cilitate high throughput measurements of tomato using ATR-FTIR. In an

experiment based on a CCD the optimal settings were found to maximize

the RPD values of the calibration curves built for the most abundant sugars

and acids in tomato. A resolution of 16 cm−1, 256 co-added scans and a

stopped flow are optimal settings. Temperature is not significant, which

allows working at room temperature.

The potential of this SIA-ATR-FTIR system as a high throughput tool

was studied in an experiment with six tomato cultivars chosen for their

difference in taste. The developed technique makes it possible to discrimi-

nate between tomato cultivars based on their specific absorption of sugars

and acids and, thus, their chemical content. Glucose and fructose are com-

pounds on which classification models are built easily due to their specific

absorbance bands. The system is able to predict the concentration of the

sugars glucose and fructose as measured using a reference technique based

on enzymatic reactions. It is also possible to predict the citric acid and

glutamate content in tomato, however correlations between measured and

predicted values are lower. The concentration of malic acid cannot be pre-

dicted due to the small concentration range present in the studied fruit. The

advantages of SIA-ATR-FTIR were demonstrated in this experiment: fast

analysis, high sample throughput, low operational costs and versatility and

simplicity of the flow injection instrumentation.

172 5.5 Conclusions

Future research involving SIA-ATR-FTIR could involve the introduc-

tion of enzymes in the system. Using these enzymes, chemical compounds

are identified individually based on their response to the enzymes, making

it possible to determine compounds in low concentrations and compounds

with absorbance spectra which are similar to each other. This enzyme based

methodology was first introduced by Lendl and Kellner (1995) for the anal-

ysis of sugars and Le Thanh and Lendl (2000) for the combined analysis of

sugars and organic acids in food samples. A next step in the development of

a flow through ATR-FTIR system for high throughput taste analysis would

be to minimize the system towards a micro-total analytical system (µ-TAS).

Chapter 6

Relation between sensory

analysis and instrumental

measurements

6.1 Introduction

Traditionally, both sensory and instrumental techniques are used to deter-

mine the taste of horticultural commodities. Although sensory panel anal-

ysis is by far the most realistic technique to obtain information on human

taste perception, it has some problems including the standardization of mea-

surements and reproducibility. Two other drawbacks of this technique are

the high cost and taste saturation of the panelist (Meilgaard et al., 2007).

Because of these drawbacks, there is a need to relate rapid, low cost and

simple methods of analysis and quality assessment to sensory panel stud-

ies in food industry. Many publications have showed the potential of ET

technology and ATR-FTIR in the determination of chemical components.

The correlation between both rapid techniques and taste as preceived by a

sensory panels or consumers, however, is less studied. Literature shows the

relation between the ET and sensory analysis in rice with different milling

yields (Tran et al., 2004), Italian wine from different vineyards (Legin et al.,

173

174 6.1 Introduction

2003) and nine commercial apple juices (Bleibaum et al., 2002). FTIR has

been coupled to sensory analysis in only one study: the classification of

Austrian pumpkin seed oil brands (Lankmayr et al., 2004).

In the previous chapters of this work, the possibilities of two ET’s and

SIA-ATR-FTIR as tools for clasification of fruit cultivars and the quan-

tification of their most important taste components were indicated. The

objective of this chapter is to study the potential of these rapid instrumen-

tal techniques to predict taste as determined by a sensory panel. This will

take place in two stages.

� The potential of the trained sensory panel to measure taste and to

respond to small differences in the chemical composition of a sample

will be evaluated. Taste compounds will be added in different con-

centrations to a weak tasting tomato juice to determine the effect of

individual taste compounds on the different sensory taste attributes.

� The potential of the ETSPU, the ASTREE ET and SIA-ATR-FTIR

to predict the different sensory taste attributes of six tomato cultivars

will be studied. The results of a sensory panel analysis will be related

to those of the instrumental techniques using preference mapping. It

reflects how well the information of the sensory analysis is described

in the results of the instrumental measurements.


and methods are described. The section discusses the samples, instrumental

techniques and sensory analysis applied in the two experiments. The results

of both experiments are given in Section 6.3. In Section 6.4 the results are

discussed and compared to the instrumental measurements and literature

findings. Concluding remarks are formulated in Section 6.5. The results of

the second experiment presented in this chapter are described in Beullens

et al. (2008b,c,a).

Relation between sensory analysis and instrumental measurements 175

6.2 Materials and methods

6.2.1 Samples

6.2.1.1 Addition of chemical taste compounds to a tomato juice

A tomato cultivar with a weak taste, Tricia, was chosen for this experiment.

Twenty fruit of this cultivar were obtained at the fruit- and vegetable Auc-

tions of Mechelen and Hoogstraten in Belgium. All tomatoes were picked

at ripeness stage 5 (light red class) (USDA, 1975) and stored at ambient

atmosphere (18 ◦C and 80% relative humidity). One week after purchase

the fruit were made into a juice and collected in a large recipient. Subse-

quently, the juice was divided over several falcon tubes of 200 mL, frozen

in liquid nitrogen and stored at −80 ◦C until the day of analysis. Before

analysis the samples were defrosted and sugars, acids, NaCl and taste en-

hancer were added to the juice. A central composite design (CCD) was used

to minimize the amount of measurements needed in the experiment. Four

factors were studied in the design at 3 levels. The four factors are sweetness

(fructose), sourness (citric acid), saltiness (NaCl, kitchensalt) and umami

(Monosodium glutamate, Ve-Tsin taste enhancer from Chinese restaurant).

Three levels are used in this experiment: (i) no addition, (ii) addition to

an average tomato and (iii) addition to an extreme tasting tomato. The

composition of the average and extreme tasting tomato were considered as

found in previous experiments (Table 3.12). The added concentrations were

determined based on knowledge of the composition of tomato cultivars found

in previous experiments. The total CCD consists of 31 measurements (Ta-

ble 6.1). The samples were analyzed using a trained sensory panel at the

Sensory Laboratory at the Vegetable Research Centre in Kruishoutem, Bel-

gium. The sugar and acid content of the Tricia tomato sample was analyzed

using an enzyme based reference technique (EHT).


Table 6.1: Addition of pure compounds to tomato juice in a Central Composite

Design (g/L).

Factor Fructose Citric acid NaCl Taste enhancer

1 0 0 0 0

2 0 0 0 2

3 0 0 0.07 0

4 0 0 0.07 2

5 0 3 0 0

6 0 3 0 2

7 0 3 0.07 0

8 0 3 0.07 2

9 13 0 0 0

10 13 0 0 2

11 13 0 0.07 0

12 13 0 0.07 2

13 13 3 0 0

14 13 3 0 2

15 13 3 0.07 0

16 13 3 0.07 2

17 0 1.5 0.04 1

18 13 1.5 0.04 1

19 6.5 0 0.04 1

20 6.5 3 0.04 1

21 6.5 1.5 0 1

22 6.5 1.5 0.07 1

23 6.5 1.5 0.04 0

24 6.5 1.5 0.04 2

25 6.5 1.5 0.04 1

26 6.5 1.5 0.04 1

27 6.5 1.5 0.04 1

28 6.5 1.5 0.04 1

29 6.5 1.5 0.04 1

30 6.5 1.5 0.04 1

31 6.5 1.5 0.04 1


6.2.1.2 Tomatoes with a wide range of tastes

Six tomato cultivars (Lycopersicon esculentum Mill.) were selected based

on their difference in taste determined by a sensory panel (Buysens, 2006a).

Since the taste of tomatoes is mainly defined by differences in sweetness and

sourness, a broad range in acid and sugar content was assured. The selected

cultivars were: Admiro, Macarena, Sunstream, Amoroso, Tricia and Clotilde

(Table 3.2). The fruit were obtained at the fruit- and vegetable Auctions of

Mechelen and Hoogstraten in Belgium. All tomatoes were picked at ripeness

stage 5 (light red class) (USDA, 1975). The fruit were stored during one day

at ambient atmosphere (18 ◦C and 80% relative humidity). The day after

purchase 10 L of tomato juice per cultivar was collected in one recipient.

Subsequently, the juice was divided over falcon tubes for instrumental and

sensory panel analysis and frozen in liquid nitrogen. The samples were stored

at −80 ◦C until measurement using SIA-ATR-FTIR (Chapter 5), two types

of ET’s (Chapter 3), two reference techniques (EHT and AAS) and a trained

sensory panel. Using reference techniques, the concentration of two sugars,

glucose and fructose, three acids, citric acid, malic acid and glutamate, and

two minerals, Na and K, in the tomato samples was determined. The frozen

tomato samples for sensory analysis were send to the Sensory Laboratory at

the Vegetable Research Centre in Kruishoutem, Belgium.

6.2.2 Sensory panel analysis

The sensory panel analysis of the samples was conducted in the Sensory Lab-

oratory at the Vegetable Research Centre in Kruishoutem, Belgium. The

sensory laboratory houses a test room with 14 individual booths constructed

according to the ISO 8589 norm (ISO, 1988). A panel of thirteen panelists

was trained over a 6-week period to evaluate sensory attributes of tomatoes

focusing on taste. A list of the sensory attributes and reference compounds

used for training is given in Table 6.2. The reference compounds were chosen

based on their importance in tomato. Only nine out of the thirteen panelists

were chosen for the experiments based on their participation and presence


Table 6.2: Sensory attributes and references used to score tomato taste.

Attribute Reference

Sweetness Fructose

Sourness Citric acid

Saltiness NaCl

Umami Monosodium-glutamate (taste enhancer from Delhaize)

during the sessions. Since the instrumental measurements described in this

thesis were performed on tomato juices, the panelists were also trained using

juices. Previous experiments at the Sensory Laboratory, however, indicated

that high correlations exist between whole tomatoes and their juices when

scored on their taste attributes by a trained panel (Buysens, 2006b). The

tomato samples of both experiments were evaluated on their sweetness, sour-

ness, saltiness and umami taste in each session. The panelists were asked to

score the taste attributes of the tomato juice contained in closed cups. The

evaluations were performed at room temperature (18−20 ◦C) under red light

to exclude the effect of color. Samples were presented in a comparative way

using a Latin square design to avoid effects of order and first position. For

each product, the assessors scored intensities for the perceived attributes on

unstructured 10 cm line scales anchored by the terms ’weak’ (0) and ’strong’

(10). Between samples panelists could rinse their mouth with water and eat

white bread without added salt. Six samples were analyzed per session.

6.2.3 Instrumental techniques

An enzymatic high throughput method (EHT) and AAS were used as ref-

erence techniques to evaluate the sugar, acid and mineral content of the

tomato juices. Details on the sample preparation and operational settings

are given in Chapter 3.

The six tomato cultivars were analyzed with two types of ET’s and SIA-

ATR-FTIR. Detailed information on the sample preparation and measure-

ment protocol is given in Chapter 3 and Chapter 5.



The data of the experiment dealing with the addition of chemical com-

pounds to a tomato juice were analyzed into detail in three steps. In a first

analysis the correlations between the four studied sensory taste attributes

were explored in panel scores correlation matrices. Second, the main effects,

interactions and lack-of-fit were studied for each attribute using response

surface regression models (SAS, 2007). And finally, using PLS2 models the

predictive capacity of the sensory panel is quantified.

Multivariate data analysis was applied for both qualitative and quanti-

tative analysis of the taste attributes of the six tomato cultivars as measured

by the sensory panel. Partial least squares-discriminant analysis (PLS-DA)

was used for data visualization and clustering of observations in the data

structure. Using this technique, intra-cultivar effects are minimized and

inter-cultivar effects are maximized. The analysis was performed on the

covariance matrix. Outliers were deleted from the analysis based on their

scores, leverages (distance to the model centre for each object summarized

over all components) and residuals (Geladi and Dabakk, 1995). The re-

sults of the PLS-DA performed on the sensory panel data were compared

to those of the reference techniques and the rapid instrumental techniques.

Preference mapping, using partial least squares analysis (PLS), was used

to correlate instrumental techniques to sensory panel analysis. The predic-

tive capacity of the instrumental techniques for sensory taste attributes was

studied using PLS2 cross-validation models (Martens and Naes, 1998; Naes

et al., 2004). For all data analysis two different computer software programs

were used: The Unscrambler version 9.1.2 (CAMO Technologies Inc., Oslo,

Norway) and SAS version 9.1 (SAS Institute Inc., Cary, USA).

180 6.3 Results

6.3 Results

6.3.1 Addition of chemical taste compounds to a tomato

juice

The correlations between the taste attributes as scored by the panelists

after addition of chemical compounds to a juice of a neutral tasting tomato

are listed in Table 6.3. The correlation matrix shows that there are small

correlations between the taste attributes. These correlations, however, are

not significant according to the probability values.

Table 6.3: Correlation matrix of four taste attributes as scored by a sensory panel

after addition of taste compounds to a tomato juice.

Taste Sweetness Sourness Saltiness Umami

Sweetness 1.0

Sourness 0.32 1.0

Saltiness 0.27 -0.12 1.0

Umami -0.30 0.070 -0.14 1.00

The main effects and interactions of the chemical compounds in the

samples to each taste attribute were studied in a response surface regression

model. The results of this analysis learn that sweetness is mainly affected

positively by the fructose and citric acid composition and to a lesser extent

by the glutamate content of the samples. The fructose content influences the

sweetness in a quadratic response with a lack-of-fit. No cross products be-

tween chemical compounds were found significant for sweet taste perception.

Sourness is positively affected by citric acid. An interaction between citric

acid and fructose exists. This indicates that fructose causes a sourness taste

suppression. A lack-of-fit was found in the analysis. Saltiness is positively

affected by the content of citric acid and the quadratic effect of NaCl. None

of the taste compounds are significant to the perception of umami taste,

although NaCl has some influence. These unexpected results for umami

could be explained by a discrepancy in the training of the panelists. The

sensory panel was trained on tasting umami using a reference compound


called ’taste enhancer’ from Delhaize. This taste enhancer is composed out

of 97% monosodium glutamate and some additives, mainly salts. During the

experiment, however, an other taste enhancer was used since the Delhaize

brand was not available anymore. The taste enhancer used as an addition to

the tomato juice in the experiment is called Ve-Tsin and was purchased at

a Chinese restaurant. Ve-Tsin is composed of only 92% monosodium gluta-

mate and 8% salt. The addition of this taste enhancer thus has an influence

both on the results found for saltiness and umami. Both for saltiness and

umami a lack-of-fit was found.

Table 6.4: PLS2 calibration models to predict sensory taste attributes built on

the known information on addition of chemical compounds. Validation models were

made using cross-validation. The offsets, RMSEC and RMSECV values are given

in g/L.


RMSECV

Sweetness Calibration 0.89 0.54 0.94 0.57

Validation 0.87 0.61 0.93 0.63 2.7

Sourness Calibration 0.87 0.54 0.93 0.56

Validation 0.82 0.74 0.91 0.64 2.5

Saltiness Calibration 0.57 1.63 0.75 0.42

Validation 0.51 1.87 0.68 0.48 1.6

Umami Calibration 0.07 3.77 0.26 0.75

Validation -0.05 4.28 -0.19 0.85 0.9

PLS2 cross-validation models were built to find correlations between the

added taste compounds and the scored sensory taste attributes. Sweetness

and sourness can be determined very well using sensory panels in a PLS2

analysis using two PC’s. The results of the analysis are shown in Table

6.4. After training the panelists on the quantification of fructose and citric

acid as references for sweetness and sourness, a very good prediction of

both sensory taste attributes was observed. The RMSE values are low,

resulting in relatively high RPD values. The RPD values of the models

made for sweetness and sourness are respectively 2.7 and 2.5. The amount

of fructose and citric acid added to the tomato juices covered a wide range,

182 6.3 Results

comparable to what is found in the Belgian tomato cultivars. The prediction

models of saltiness and umami are less good. This could be explained by the

discrepancy in the training of the sensory panel with the reference compound

which was described earlier. The high offsets found in all models are a result

of the chemical composition of the tomatoes to which chemical compounds

were added. The composition of this tomato was not taken into account in

this analysis.

6.3.2 Tomatoes with a wide range of tastes

According to the panelists, Amoroso is the most sweet tasting tomato and

has a strong umami taste (Tabel 6.5). Together with Tricia and Clotilde, this

cultivar, is scored as not sour. Admiro is more sour than the other cultivars.

Admiro, Tricia and Clotilde get low scores for umami. The differences in

saltiness are smaller than for the other tastes.

Table 6.5: Average results of panel scores of the tomato samples (average ±standard deviation, scores between 0 and 10).

Cultivar Sweetness Sourness Umami Saltiness

Admiro 2.2±0.3 5.6±0.2 2.9±0.2 3.7±0.5

Macarena 3.3±0.5 4.0±0.5 3.3±0.6 3.8±0.5

Sunstream 4.7±0.3 4.4±0.3 4.3±0.7 4.1±0.5

Amoroso 6.8±0.7 3.1±0.3 4.6±0.1 3.4±0.5

Tricia 2.3±0.3 3.3±0.4 2.3±0.8 2.6±0.5

Clotilde 3.6±0.7 3.4±0.2 2.7±0.2 2.6±0.2

A PLS-DA was performed on the panel scores of the six tomato cultivars.

The results are shown in Figure 6.1. The score plot shows that Amoroso is

separated from the other cultivars along the axis of the first PC, which is

highly correlated with sweetness. This cultivar was also scored as a sweet

tomato by the sensory panel (Table 6.5). The results of the reference analysis

indicated that Amoroso contains high concentrations of glucose and fructose

and a low concentration of malic acid and that Amoroso is highly correlated

with glutamate, glucose and fructose.


-2

-1

0

1

2

-5 -4 -3 -2 -1 0 1 2 3

PC

2

PC 1


A

Sweetness

Amoroso

Clotilde Tricia

0 0

0.2

0.4

0.6

0.8

1.0

PC

2

PC 1

SournessSaltiness

Umami

Admiro

Macarena

Sunstream

-1.0

-0.8

-0.6

-0.4

-0.2

0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

B

Figure 6.1: Score plot and correlation loadings of the PLS-DA based on the

sensory panel scores (X-expl. (70%, 22%); Y-expl. (20%, 19%)).

184 6.3 Results

Malic acidCitric acid

K

SOURNESSSALTINESS

UMAMI0.2

0.4

0.6

0.8

1.0

PC

2

PC 1Glucose

Fructose

Glutamate

Na

K

SWEETNESS

-1.0

-0.8

-0.6

-0.4

-0.2

0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

PC 1

Figure 6.2: Correlation loadings of the first two PC’s based on the sensory panel

scores and measurements of the reference techniques EHT and AAS (X-expl. (99%,

1%); Y-expl. (66%, 19%)).

The previous experiment and the correlation loadings between the panel

scores and the reference measurements (Figure 6.2) show that sweetness is

highly correlated with the glucose and fructose content. Admiro is posi-

tioned in the second quadrant of the score plot and is also separated from

the other cultivars. This cultivar is highly correlated with sourness. This

sourness, however, can not be explained directly by the chemical composi-

tion of this cultivar. Admiro does not contain high concentrations of citric

acid and malic acid. Figure 6.2 shows that sourness is not correlated with

citric acid, the most abundant acid in tomato. The samples of both Admiro

and Amoroso are all classified correctly, which is in contrast to the other

cultivars. Clotilde and Tricia are separated from the other cultivars but not

from each other. Only 25% of the samples of Tricia are classified correctly

by the sensory panel. Sunstream and Macarena are also separated from the


other cultivars. None of these four cultivars is highly correlated with any of

the sensory taste attributes in the plane of the first two PC’s. The sensory

panel is not able to classify the samples belonging to Clotilde, Sunstream

and Macarena correctly.

The calibration and validation models of the panel scores of the four sen-

sory taste attributes as predicted by the reference measurements are shown

in Table 6.6. Three PC’s were used to build these models. All models show

high slopes, low offsets and high correlations between the predicted and

measured values. The RMSEC and RMSECV values are low, resulting in

high RPD values for all models. The RPD of sweetness, sourness, saltiness

and umami are high with values of respectively 3.4, 4.6, 3.3 and 3.6. The

correlation loadings plot (Figure 6.2) shows that sweetness is highly corre-

lated with glucose and fructose as measured using the EHT method. Some

correlation also occurs between umami and glutamate and saltiness and Na.

Sourness is correlated with malic acid and not citric acid, which is surprising

since the panelists were trained on tasting sourness using citric acid. The

large differences in the human detection threshold of sourness between malic

acid and citric acid might be an explanation for this result (Stahl, 1973).

Table 6.6: PLS2 calibration models to predict taste built on the results of the

measurements of the the enzymatic reference technique and AAS. Validation models

are built using cross-validation. The offsets, RMSEC and RMSECV values are given

in g/L.


RMSECV

Sweetness Calibration 0.91 0.33 0.96 0.49

Validation 0.89 0.40 0.93 0.62 3.4

Sourness Calibration 0.86 0.55 0.93 0.34

Validation 0.83 0.66 0.90 0.41 4.6

Saltiness Calibration 0.63 1.22 0.80 0.42

Validation 0.56 1.50 0.69 0.51 3.3

Umami Calibration 0.79 0.72 0.89 0.46

Validation 0.76 0.82 0.84 0.52 3.6

186 6.3 Results

Table 6.7: Correlations (R), RMSECV and RPD values found in the PLS2 models

to predict taste as scored by a sensory panel using the ETSPU, ASTREE ET

and SIA-ATR-FTIR. Cross validation was used to build validation models. The

RMSECV values are given as scores between 0 and 10.

Taste ETSPU ASTREE ET SIA-ATR-FTIR

Sweetness R 0.48 0.80 0.94

RMSECV 1.47 1.22 0.58

RPD 1.4 1.7 3.6

Sourness R 0.76 0.70 0.91

RMSECV 0.64 0.69 0.39

RPD 2.9 2.7 4.8

Saltiness R 0.84 0.94 0.87

RMSECV 0.42 0.41 0.50

RPD 4.4 4.5 3.7

Umami R 0.57 0.85 0.77

RMSECV 0.80 0.69 0.47

RPD 2.4 2.4 3.5

The potential of both the ETSPU and the ASTREE ET to predict sen-

sory panel scores is studied in a PLS2 analysis. The main results of the

PLS2 validation models of both multisensor systems using four PC’s are

shown in Table 6.7. The ETSPU gives very good results for all calibration

models (results not shown). All slopes and correlations are close to 1 and

the offsets and RMSEC values are low. Low values for the slopes and high

values for the offset are found in all cross-validation models. The correla-

tions found in the PLS models of sourness and saltiness, respectively 0.76

and 0.84, are acceptable, but the correlations between the other two sensory

taste attributes, sweetness and umami, and the ETSPU are low. RMSECV

values are low except for the prediction model of sweetness. This is re-

flected in the RPD value of the prediction model. All models show RPD

values higher than 2, except for the model for sweetness which has an RPD

value of 1.4. The correlation loadings plot (Figure 6.3) shows that sweetness

is positively correlated with sensors 8, 9, 10 and 11, which are the cationic

sensors. A negative correlation exists between sweetness and sensors 1, 2, 5


and 7, which are all anionic sensors. Since more than one sensor is correlated

with sweetness, the question of sensor selection rises. Sourness is correlated

negatively with sensor 16. By selecting only two sensors for sweetness and

sensor 16 for sourness, the main taste attributes of tomatoes could be pre-

dicted. The possibilities of sensor selection for the prediction of sweetness

were evaluated in a PLS1 analysis. Using only sensor 1 and sensor 10, a

prediction model for sweetness with a correlation of 84%, RMSECV of 0.88

and RPD value of 2.4 is created. This result is better than when all sensors

of the ETSPU are used. The two other tastes, saltiness and umami, are not

highly correlated to any of the sensors of the ETSPU.

Sensor 3

Sensor 4

Sensor 6

Sensor 8

Sensor 10Sensor 11Sensor 15

Sensor 16

Sweetness

Umami0 0

0.2

0.4

0.6

0.8

1.0

PC

2

PC 1

Sensor 1Sensor 2

Sensor 5 Sensor 7

Sensor 9

Sensor 17

Sourness

Saltiness

-1.0

-0.8

-0.6

-0.4

-0.2

0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0


scores and measurements with the ETSPU (X-expl. (82%, 10%); Y-expl. (47%,

18%)).

188 6.3 Results

ZZ

BA

BB

CA

GA

HA

Sweetness

Sourness

SaltinessUmami

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

PC

2

PC 1


scores and measurements with the ASTREE ET (X-expl. (78%, 16%); Y-expl.

(36%, 14%)).

A PLS2 analysis was also performed on the data from the ASTREE ET

(Table 6.7). The results of the PLS calibration models between the sensory

taste attributes and the ASTREE ET (results not shown) are comparable

to those of the ETSPU. All slopes and correlations are again close to 1 and

offsets and RMSEC values are low. The validation models, however, are

different from those of the multisensor system developed at the University

of Saint-Petersburg. The slopes are low and the offsets are high, but the

correlations between the ASTREE ET and sweetness, sourness, saltiness and

umami are high, respectively 0.80, 0.70, 0.94 and 0.85. All RMSECV values

are low, except for the prediction model of sweetness. This is again reflected

in the RPD values. The model for sweetness has a value of 1.7, while all

other models have RPD values higher than 2. The correlation loadings plot

(Figure 6.4) shows that sweetness is positively correlated with sensor CA


and negatively with sensor GA. Sourness shows a negative correlation with

sensor BB. Saltiness and umami are not highly correlated to any of the

sensors of the ASTREE ET.

1751

1689

1612

1542

1450 13731349

Sweetness

Sourness

Saltiness

Umami13261265

1218

11031018

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

PC 1

PC

2


scores and measurements with SIA-ATR-FTIR (X-expl. (97%, 2%); Y-expl. ( 52%,

19%)).

The main results of the PLS2 analysis in which the absorbances at se-

lected wavenumbers of the first derivative SIA-ATR-FTIR spectra are used

to predict sensory panel scores are listed in Table 6.7. As with the ET

systems, four PC’s were needed for the model. The correlations between

the panel scores and the predicted sweetness and sourness are high. Both

the calibration (results not shown) and validation models for sweetness and

sourness show correlations equal to or higher than 0.90. The RMSEC and

RMSECV values are low and RPD values are high for all studied sensory

taste attributes. The prediction models for umami and saltiness are some-

what less accurate with lower correlations between the measured and pre-

190 6.4 Discussion

dicted score but still acceptable. This is explained by the fact that only a

small part of the scale was used to score by the panelists, indicating that the

differences in umami and salty taste between the cultivars are small. The

RPD values of the prediction models of both umami and saltiness, however,

are higher than 2. The PLS2 results indicate that while FTIR seems to be

a very accurate technique to predict sweetness and sourness, the ET sys-

tems give the best predictions of saltiness. The correlation loadings plot

(Figure 6.5) shows that sweetness and umami are both correlated with 1103

cm−1 and 1018 cm−1, 1450 cm−1, 1373 cm−1, 1349 cm−1, 1326 cm−1 and

1265 cm−1. At these wavenumbers important C-O stretching vibrations are

found. Saltiness shows some correlation with 1612 cm−1 and 1542 cm−1 and

sourness is correlated with wavenumbers 1689 cm−1 and 1751 cm−1, which

are situated in IR region with C=O stretching vibrations.

6.4 Discussion

6.4.1 Addition of chemical compounds to a tomato juice

The correlations and interactions of taste compounds to the four sensory

taste attributes of tomatoes were studied in a first experiment. Hereto,

different amounts of fructose, citric acid, NaCl and a taste enhancer Ve-

Tsin were added to a weak tasting tomato juice.

The perception of sweetness is in general influenced by the level of fruc-

tose, citric acid and glutamate in the samples. Stevens et al. (1977) and

Fernandez-Ruiz et al. (2004) found that when the total sugar content of

a food product is low, citric acid reduces the perceived sweetness. But in

food products with high sugar concentrations, however, citric acid has a

sweetness increasing effect. In the experiment described in this thesis, the

strongest perception of sweetness was found at high fructose and high citric

acid levels. Also, the presence of some salts is found to intensify sweetness

(Stevens et al., 1977; Stevenson et al., 1999). The presence of 8% salt in

the taste enhancer Ve-Tsin could be an explanation for the positive effect of


this taste enhancer on sweetness.

The sourness as perceived by the panelists is related to the level of citric

acid and the interaction of citric acid and fructose. The fructose content

of the sample greatly affects sourness. Stevens et al. (1977) and Baldwin

et al. (1998) described that in case of samples with a low citric acid and

glucose content, fructose is found to reduce sour taste. With high citric

acid and glucose concentrations, this effect is not valid. According to the

authors, sugars have a much less intense effect on the perception of sourness

than acids on sweetness. Despite the effect of citric acid on sweetness and

of fructose on sourness, both tastes can be measured perfectly in tomato

samples using a trained sensory panel.

The perception of saltiness in tomatoes seems to be positively affected

by the citric acid and NaCl content of the samples. The role of citric acid

can be explained by the fact that saltiness and sourness are detected by a

common family of proteins (Ramos Da Conceicao Neta et al., 2007).

Finally, only NaCl seems to affect the perception of umami significantly.

This is an odd result, which could maybe be explained by the inconsistency

between the training of the panelists and the experiment. During the train-

ing, a taste enhancer from Delhaize was used to train the panelist in tasting

umami. Since this taste enhancer was no longer available, an alternative

was found in the taste enhancer Ve-Tsin from a Chinese restaurant. The

two taste enhancers contained different amounts of monosodium glutamate

and salts, which could explain the difficulties of the panelists to taste both

saltiness and umami. This hypothesis, however, has not been confirmed yet.

6.4.2 Tomatoes with a wide range of tastes

Using a trained sensory panel, it is possible to distinguish between tomato

cultivars. The cultivars are classified according to their taste and taste com-

pounds. The results of the sensory panel and the reference analysis with

EHT and AAS can be related to each other. Amoroso, a sweet tomato

which also contains high sugar concentrations, is classified separately from

192 6.4 Discussion

the other cultivars. The separation occurs based on the taste and chemical

composition of this cultivar. The cultivar Admiro is highly correlated with

sourness as perceived by the sensory panel. This perceived sourness, how-

ever, can not be explained directly by the chemical composition of the cul-

tivar. Admiro does not contain a high concentration of citric acid. Stevens

et al. (1977) and Baldwin et al. (1998) described that in food products with

a low citric acid and glucose content, which is the case for Admiro, fructose

is found to reduce sour taste. Since the fructose content of this cultivar is

also low, this will not affect the sourness significantly. The content of malic

acid, on the other hand, could be an explanation for the sourness of Admiro.

Malic acid is found to be up to 14% more sour (w/v) than citric acid (Stevens

et al., 1977). The relatively high content of this acid in Admiro explains the

high scores given by the panelists to the attribute sourness. The other four

cultivars are not highly correlated with any of the taste attributes.

In preference mapping, the PLS2 models to predict the four taste at-

tributes which were scored by the sensory panel show different results for

the different rapid techniques. Using the reference techniques, EHT and

AAS, it is possible to predict all taste attributes. Both ET’s are able to

predict three taste attributes: sourness, saltiness and umami. The bad pre-

diction of sweetness by both ET’s is unexpected since only the ASTREE

ET failed in predicting taste compounds (Chapter 3). From this can be

concluded that the ASTREE ET is developed specifically for the prediction

of tastes and not individual taste compounds, which was also stated by the

Alpha M.O.S. company. Bleibaum et al. (2002) found good predictions of

sweetness in apple juices. Apples, however, have a higher sugar content than

tomatoes, which could explain this discrepancy. The selection of two sensors

from the array made it possible for the ETSPU to predict sweetness accu-

rately. The selection of sensors was also discussed by Legin et al. (1997),

who mentioned that the choice of an optimized sensor array is crucial for

analysis. These authors indicated that the chemical sensors need to be pre-

pared using the best sensing materials and that the correct sensors need to

be selected to allow the highest available sensitivity to certain species and

significant chemical durability and signal stability of the sensors.


Until now, the correlation between (SIA-)ATR-FTIR and sensory panels

for the analysis of taste has not been investigated. Here, sweetness and sour-

ness are accurately predicted in tomato using SIA-ATR-FTIR, which gives

an indication of the potential of this technique to determine the main taste

of many fruit (Petro-Truza, 1987). The correlation loadings showed a high

correlation between sweetness and some wavenumbers which located in the

region of the absorbance spectrum where C-O bonds vibrate. This indicates

that sweetness is correlated with the sugars present in the samples, which

was also found from the results of the reference techniques. The prediction of

saltiness and umami using SIA-ATR-FTIR is also very good, though both

potentiometric ET systems seem to be more accurate in predicting salty

taste.

6.5 Conclusions

The potential of two newly developed instrumental techniques to predict

taste attributes as determined by a sensory panel was evaluated in this

chapter.

In a first experiment the response of the panelists to small differences in

the chemical composition of fruit was investigated. Different concentrations

of fructose, citric acid, NaCl and a taste enhancer were added to a weak

tasting tomato cultivar, Tricia, which was selected for this experiment. The

sensory panel was able to score the sweetness and sourness of the samples

accurately. High correlations were found between fructose and citric acid

on the one hand and sweetness and sourness on the other hand. Also, the

addition of a taste enhancer seemed to influence the perception of sweetness.

The determination of saltiness and umami by the panelists was correlated

with the NaCl content of the samples.

In a second experiment, the ability of two types of ET’s and SIA-ATR-

FTIR to predict taste attributes was studied using preference mapping. The

results of the rapid instrumental measurements, together with those of EHT

and AAS analysis, were compared to the results of sensory panel studies in

194 6.5 Conclusions

an experiment with six tomato cultivars. Using the reference techniques it

was possible to predict all taste attributes. High correlations were found

between the concentrations of the individual chemical compounds and the

taste attributes. Using the ETSPU and the ASTREE ET, three of the scored

tastes, sourness, saltiness and umami taste, were predicted accurately. The

prediction of sweetness by the ETSPU increased slightly after selection of a

few sensors. Using SIA-ATR-FTIR is it possible to make accurate predic-

tions on the taste of the tomatoes. Good models are found for sweetness,

sourness, saltiness and umami taste. Although the results of the prediction

of saltiness using SIA-ATR-FTIR are not as good as those of both ET’s,

FTIR showed it has potential as screening technique to analyze the taste of

a large amount of juices in a short time period. The analysis of the taste

and taste components of one sample can be reduced to less than one minute

using this rapid technique.

Chapter 7

General conclusions and

future work

7.1 General conclusions

Taste is one of the most subjective quality characteristics of food products.

Five gustatory perceptions, sweetness, sourness, saltiness, umami and bit-

terness, caused by soluble substances in the mouth describe the overall taste

of a product (Meilgaard et al., 2007). The five basic tastes are mainly caused

by the presence of sugars, organic acids, salts, monosodium-glutamate, phe-

nolics and alkaloids. Various techniques are used to determine the chemical

content of foodstuff. Sugars and acids can be determined by HPLC or

GC(-MS). Both techniques require expensive apparatus and demand a con-

siderable amount of time per measurement (Molnar-Perl, 1999). Another

method of determining specific sugars and acids is by enzymatic analysis.

For enzymatic assays, sample preparation is simple and the only apparatus

required is a spectrophotometer (Vermeir et al., 2007). The sensation of

a taste can, however, not simply be explained by the presence of a com-

pound. A traditional method for taste analysis is sensory evaluation. This

technique is used to measure those characteristics of foods and materials in

the way that they are perceived by the human senses. Although sensory

195

196 7.1 General conclusions

panel analysis is by far the most realistic technique to obtain information

on human taste perception, it has some problems including the correctness

of training, standardization of measurements, stability and reproducibility.

Other drawbacks are the high cost and taste saturation of the panelist (Meil-

gaard et al., 2007). Because of these drawbacks, there is a need to replace

traditional instrumental techniques in the analysis of taste compounds and

to relate rapid, low cost and simple methods of analysis to sensory panel

studies in food industry. In the last decade, arrays of liquid sensors, called

electronic tongues (ET’s), were developed. The main advantages of ET’s are

the low cost, easy-to-handle measurement set-up and speed of the measure-

ments (Vlasov et al., 2002). Another alternative for traditional techniques is

FTIR spectroscopy, which is a well-established technique in chemical anal-

ysis. If combined with ATR, this technique offers great advantages for food

analysis (Griffiths and de Haseth, 2007).

The objective of this work was to study the potential of rapid and objec-

tive measurement techniques for taste profiling of fruit and vegetables. This

objective was achieved through several subobjectives.

In a first step, ET technology and FTIR spectroscopy were evaluated as

rapid instrumental techniques for the classification of fruit samples based on

their chemical composition. Both the ETSPU and ATR-FTIR proved to

be able to distinguish between apple and tomato based on their chemical

composition. Differences in the main taste compounds of tomato can be

detected by both rapid techniques within minutes. An SIA system was de-

veloped and optimized to automize the acquisition of ATR-FTIR spectra.

Two types of ET’s, ETSPU and ASTREE ET, and SIA-ATR-FTIR were

compared for the classification of tomatoes with very distinct tastes. De-

spite the considerable differences in measurement protocol, both multisensor

systems and SIA-ATR-FTIR showed to be able to classify tomato cultivars

based on their sugar, acid and mineral content.

Second, the ability of the ET and (SIA-)ATR-FTIR to quantify individ-

ual taste compounds in fruit was studied. The selection of the correct set of

sensors showed to be crucial in the prediction of chemical compounds us-

General conclusions and future work 197

ing the ETSPU. After optimizing the sensor array, the ETSPU, unlike the

ASTREE ET, was able to predict individual compounds in a tomato ma-

trix. Using (SIA-)ATR-FTIR, specific vibrations of chemical bonds made it

possible to relate taste compounds directly to the absorbance spectra. The

matrix, however, seemed to influence the predictive ability of this technique

largely. ATR-FTIR, both as a static and dynamic flow through system,

proved to be accurate in predicting taste compounds.

As mentioned before, taste cannot simply be described by the chemical

composition of a food product. Therefore the potential of ET and SIA-

ATR-FTIR to determine the taste of fruit as perceived by a trained sensory

panel was examined. Using the ETSPU and the ASTREE ET three taste

attributes, sourness, saltiness and umami, were predicted accurately within

minutes. The prediction of sweetness is only acceptable using the ETSPU

after selection of specific sensors. SIA-ATR-FTIR is able to make accurate

predictions on sweetness, sourness, saltiness and umami. Although the re-

sults of the prediction of saltiness using SIA-ATR-FTIR are not as good

as those of both ET’s, FTIR showed to have potential of being a screening

technique to analyze the taste of a large amount of samples in a short time

period. This shows that the analysis of taste attributes and taste compo-

nents of one sample can be reduced to less than one minute using this rapid

technique.

And finally, after concluding that both ET technology and FTIR spec-

troscopy are good techniques to classify samples based on their taste com-

pounds and taste and to quantify taste compounds and taste, the possibilities

of the both techniques as tools for quality control of fruit juices were inves-

tigated in this thesis. Both the ETSPU and ATR-FTIR were able to group

multifruit juices and the individual syrups they are made off. However,

information of the extra compounds present in the multifruit juices is nec-

essary to make good classification models to quantify the concentrations of

constituent syrups. Individual syrups were predicted very accurately in the

multifruit juices using the ETSPU and ATR-FTIR. Both rapid techniques

made it possible to predict even very low concentrations of syrup in the

complex multifruit juice. The ET and ATR-FTIR showed to be promising

198 7.2 Future work

techniques for quality control because of their good performance, easy use

and detection speed.

7.2 Future work

In future work related to this thesis, following aspects could be addressed:

� The selection of the sensor array of the ETSPU was important both

in the prediction of individual taste compounds and taste. Future

research would involve the optimization of the sensor materials and

selection of sensors, so that good predictions could be made using a

sensor array with a minimal amount of sensors.

� To enhance the quantification potential of taste compounds using

(SIA-)ATR-FTIR, without adding an extensive separation of the com-

pound of interest, advanced statistical techniques need to be evalu-

ated. The recent development of a new class of multivariate calibra-

tion methods, called augmented classical least squares (ACLS) which

is an extension of the classical least squares model (CLS) to handle

cases where not all compounds contributing to the absorbance signal

are explicitly included in the calibration models, could make the pre-

diction of taste compounds more accurate, even when they are present

in low concentrations.

� Future research involving SIA-ATR-FTIR could involve the introduc-

tion of enzymes in the system. Using these enzymes, the individual

chemical compounds can be identified separately, making it possible

to determine compounds in low concentrations and compounds with

absorbance spectra which are similar to each other. A next step in the

development of a flow through ATR-FTIR system for high throughput

taste analysis would be to minimize the system towards a micro-total

analytical system (µ-TAS).

� After proving their potential to classify tomato cultivars, both the

ET and (SIA-)ATR-FTIR could be introduced in breeding or cultivar

General conclusions and future work 199

selection programs. Using the rapid techniques, cultivars with extreme

tastes would be identified easily, making the need for sensory analysis

less important.

� Using the rapid instrumental techniques described in this thesis, the

influence of growing techniques on taste could be studied as a practi-

cal application. The variability rising from differences in cultivation

techniques should be minimized to guarantee high quality fruit with a

constant taste. In the framework of a research project, the influence

of grafting, the distance between stems, the harvesting frequency and

the addition of salt in hydroculture on tomato taste will be studied.

� To study the potential of the ET and ATR-FTIR as rapid tools for

quality control of fruit juices, extra information on the composition

of the multifruit juices would be required. After performing measure-

ments on the extra compounds, like the aloe vera puree, vitamins

and minerals, better classification models might result with both tech-

niques.

200 7.2 Future work

Bibliography

AlphaM.O.S. (2001a). α astree electronic tongue user manual.

AlphaM.O.S. (2001b). α astree sensor technical note.

AlphaM.O.S. (2006). Electronic nose and tongue. Alcoholic and non alco-

holic beverages. Alpha M.O.S. Newsletter, summer 2006.

Armenta, S., Garrigues, S., de la Guardia, M., and Rondeau, P. (2005a).

Attenuated total reflection-fourier transform infrared analysis of the fer-

mentation process of pineapple. Analytica Chimica Acta, 545:99–106.

Armenta, S., Quintas, G., Garrigues, S., and de la Guardia, M. (2005b).

Mid-infrared and raman spectrometry for quality control of pesticide for-

mulations. TRAC, 24:772–781.

Armenta, S., Quintas, G., Garrigues, S., and de la Guardia, M. (2005c).

Mid-infrared and raman spectrometry for quality control of pesticide for-

mulations. Trends in Analytical Chemistry, 24:772–781.

Auerswald, H., Peters, P., Bruckner, B., Krumbein, A., and Kuchenbuch,

R. (1999). Sensory analysis and instrumental measurmeents of short-term

stored tomatoes (Lycopersicon esculentum mill.). Postharvest Biology and

Technology, 15:323–334.

Ayora-Canada, M. and Lendl, B. (2000). Sheath-flow fourier transform

infrared spectrometry for the simultaneous determination of citric, malic

and tartaric acid in soft drinks. Analytica Chimica Acta, 417:41–50.

201

202 BIBLIOGRAPHY

Back, D., Michalska, D., and Polavarapu, P. (1984). Fourier transform

infrared spectroscopy as a powerful tool for the study of carbohydrates in

aqueous solutions. Applied Spectroscopy, 38:173–180.

Baldwin, E., Nisperos-Carriedo, O., Baker, R., and Scott, J. (1991). Quanti-

tative analysis of flavor parameters in six florida tomato cultivars (Lycop-

ersicon esculentum mill.). Journal of Agricultural and Food Chemistry,

39:1135–1140.

Baldwin, E., Scott, J., Einstein, M., Malundo, T., Carr, B., Shewfelt, R.,

and Tandon, K. (1998). Relationship between sensory and instrumental

analysis for tomato flavour. Journal of the American Society for Horticu-

tural Sciences, 123:906–915.

Beullens, K., Buysens, S., Cap, N., Meszaros, P., Kirsanov, D., Legin, A.,

Nicolaı, B., and Lammertyn, J. (2008a). Relating sensory analysis to

electronic tongues and atr-ftir for tomato taste profiling. Journal of Agri-

cultural and Food Chemistry, (Submitted).

Beullens, K., Irudayaraj, J., Kirsanov, D., Legin, A., Nicolaı, B., and Lam-

mertyn, J. (2005a). The electronic tongue and ftir-atr as fast techniques

for taste profiling. Proceedings of ISOEN, 9th International Symposium

on Olfaction and Electronic Nose, Barcelona, Spain, pages 312–315.

Beullens, K., Irudayaraj, J., Kirsanov, D., Legin, A., Nicolaı, B., and Lam-

mertyn, J. (2005b). The electronic tongue and ftir for rapid taste profiling

in food. KVCV Voedselchemie in Vlaanderen - Trends in Levensmiddele-

nanalyse, Gent, Belgium, pages 89–90.

Beullens, K., Irudayaraj, J., Nicolaı, B., Kirsanov, D., Legin, A., and Lam-

mertyn, J. (2004). Novel techniques for fast taste profiling of tomatoes.

Communications in Agricultural and Applied Biological Sciences, 10th

PhD Symposium on Applied biological Sciences, Gent, Belgium, pages

57–60.

Beullens, K., Kirsanov, D., Irudayaraj, J., Rudnitskaya, A., Legin, A.,

Nicolaı, B., and Lammertyn, J. (2006a). The electronic tongue and atr-ftir

BIBLIOGRAPHY 203

for rapid detection of sugars and acids in tomatoes. Sensors and Actuators

B, 116:107–115.

Beullens, K., Meszaros, P., Kirsanov, D., Legin, A., Buysens, S., Cap, N.,

Nicolaı, B., and Lammertyn, J. (2008b). Analysis of tomato taste using

two types of electronic tongues. Sensors and Actuators B, (Accepted).

Beullens, K., Meszaros, P., Kirsanov, D., Legin, A., Nicolaı, B., and Lam-

mertyn, J. (2007a). Analysis of tomato taste using two types of electronic

tongues. Proceedings of ISOEN, 10th International Symposium on Olfac-

tion and Electronic Nose, Saint-Petersburg, Russia, pages 146–147.

Beullens, K., Meszaros, P., Kirsanov, D., Legin, A., Nicolaı, B., and Lam-

mertyn, J. (2007b). Taste analysis of tomato juices using two different

multisensor systems. Book of Abstracts, Euroanalysis X, Antwerp, Bel-

gium, page 591.

Beullens, K., Meszaros, P., Nicolaı, B., and Lammertyn, J. (2008c). Devel-

opment of a sequential injection atr-ftir system to analyze belgian tomato

cultivars. Analytical and Bioanalytical Chemistry, (Submitted).

Beullens, K., Sels, B., Schoonheydt, R., Nicolaı, B., and Lammertyn, J.

(2006b). Determination of sugars and organic acids in tomato by means

of flow injection atr-ftir and emsc. Conference proceedings, 10th Interna-

tional Conference on Flow Analysis, Porto, Portugal, page 240.

Beullens, K., Vermeir, S., Nicolaı, B., and Lammertyn, J. (2007c). Atr-ftir

as a rapid technique for taste profiling of fruit juices. Book of Abstracts,

Euroanalysis X, Antwerp, Belgium, page 436.

Bleibaum, R., Stone, H., Tan, T., Labeche, S., Saint-Martin, E., and Isz,

S. (2002). Comparison of sensory and consumer results with electronic

nose and tongue sensors for apple juice. Food Quality and Preference,

13:409–422.

Bodecchi, L., Cocchi, M., Malagoli, M., Manfredini, M., and Marchetti, A.

(2005). Application of infrared spectroscopy and multivariate quality-

204 BIBLIOGRAPHY

control methods in pvc manufacturing. Analytica chimica Acta, 554:207–

217.

Breslin, P., Kemp, S., and Beauchamp, G. (1994). Single sweetness signal.

Nature, 369:447–448.

Buysens, S. (2006a). Personal communication.

Buysens, S. (2006b). Personal communication.

Campbell, J., Hansen, R., and Wilson, J. (1999). Cost-effective colorimet-

ric microtitre plate enzymatic assays for sucrose, glucose and fructose in

sugarcane tissue extracts. Journal of the Science of Food and Agriculture,

79:232–236.

Carolei, L. and Gutz, I. (2005). Simultaneous determination of three surfac-

tants and water in shampoo and liquid soap by atr-ftir. Talanta, 66:118–

124.

Chalmers, J. and Griffiths, P. (2002). Handbook of vibrational spectroscopy.

John Wiley and Sons Inc., Hoboken, N.J., U.S.A.

Ciosek, P. and Wroblewski, W. (2007). Sensor arrays for liquid sampling -

electronic tongue systems. Analyst, 132:963–978.

Daghbouche, Y., Garrigues, S., Vidal, M., and de la Guardia, M. (1997).

Flow injection fourier transform infrared determination of caffeine in soft

drinks. Analytical Chemistry, 69:1086–1091.

Davies, J. (1964). Effect of nitrogen, phosphorus and potassium fertilizers

on the non-volatile organic acids of tomato fruit. Journal of the Science

of Food and Agriculture, 15:665–673.

Davies, J. and Kempton, R. (1975). Changes in the individual sugars of

tomato fruit during ripening. Journal of the Science of Food and Agricul-

ture, 26:1103–1110.

Davies, J. and Winsor, G. (1967). Effect of nitrogen, phosphorus, potassium,

magnesium and liming on the composition of tomato fruit. Journal of the

Science of Food and Agriculture, 18:459–466.

BIBLIOGRAPHY 205

De Bruyn, J., Garretsen, F., and Kooistra, E. (1971). Variation in taste

and chemical composition of the tomato (Lycopersicon esculentum mill.).

Euphytica, 20:214–227.

De Lene Mirouze, F., Boulou, J., Dupuy, N., Meurens, M., Huvenne, J., and

Legrand, P. (1993). Quantitative analysis of glucose syrups by atr/ft-ir

spectroscopy. Applied Spectroscopy, 47:1187–1191.

Deisingh, A., Stone, D., and Thompson, M. (2004). Applications of elec-

tronic noses and tongues in food analysis. International Journal of Food

Science and Technology, 39:587–604.

Di Natale, C., Paolesse, R., Macagnano, A., Mantini, A., D’Amico, A., Le-

gin, A., Lvova, L., Rudnitskaya, A., and Vlasov, Y. (2000). Electronic

nose and electronic tongue integration for improved classification of clin-

ical and food samples. Sensors and Actuators B, 64:15–21.

Dogan, A., Siyakus, G., and Severcan, F. (2007). Ftir spectroscopic char-

acterization of irradiated hazelnut (Corylus avellana l.). Food Chemistry,

100:1106–1114.

Drewnowksi, A. (2001). The science and complexty of bitter taste. Nutrition

Reviews, 59:163–169.

Economou, A. (2005). Sequential-injection analysis (sia): a useful tool for

on-line sample-handling and pre-treatment. TRAC, 24:416–425.

Edelmann, A., Diewok, J., Rodriguez Baena, J., and Lendl, B. (2003). High-

performance liquid chromatography with diamond atr-ftir detection for

the determination of carbohydrates, alcohols and organic acids in red

wine. Analytical and Bioanalytical Chemistry, 376:92–97.

Eggins, B. (2002). Chemical sensors and biosensors. Wiley, New York, N.Y.,

U.S.A.

Fernandez-Ruiz, V., Sanchez-Mata, M., Camara, M., Torija, M., Chaya, C.,

Galiana-Balaguer, L., Rosello, S., and Neuz, F. (2004). Internal quality

characterisation of fresh tomato fruits. HortScience, 39:339–345.

206 BIBLIOGRAPHY

Gallardo, J., Alegret, S., and del Valle, M. (2005). Application of a potentio-

metric electronic tongue as a classification tool in food analysis. Talanta,

66:1303–1309.

Gallignani, M. and Brunetto, M. (2004). Infrared detection in flow analysis

- developments and trends (review). Talanta, 64:1127–1146.

Gardner, J. and Bartlett, P. (1994). A brief history of electronic noses.

Sensors and Actuators B, 18–19:211–220.

Garrigues, J., Akssira, M., Rambla, F., Garrigues, S., and de la Guardia,

M. (2000). Direct atr-ftir determination of sucrose in beet root. Talanta,

51:247–255.

Geladi, P. and Dabakk, E. (1995). An overview of chemometrics applications

in near infrared spectrometry. Journal of Near Infrared Spectroscopy,

3:119–132.

Griffiths, P. and de Haseth, J. (2007). Fourier transform infrared spectrom-

etry - second edition. John Wiley and Sons Inc., Hoboken, N.J., U.S.A.

Gunasekaran, S. (2001). Nondestructive food evaluation: techniques to ana-

lyze properties and quality. M. Dekker, New York, N.Y., U.S.A.

Hansen, E. and Wang, J. (2004). The three generations of flow injection

analysis. Analytical Letters, 37:345–359.

Haswell, S. (1991). Atomic absorption spectrometry: theory, design and

applications. Elsevier, Amsterdam, The Netherlands.

He, J., Rodriguez-Saona, L., and Giusti, M. (2007). Midinfrared spec-

troscopy for juice authentication - rapid differentiation of commercial

juices. Journal of Agricultural and Food Chemistry, 55:4443–4452.

Holmin, S., Krantz-Rulcker, C., Lundstrom, I., and Winquist, F. (2001).

Drift correction of electronic tongue responses. Measurement Science and


Hootman, R. (1992). Manual on descriptive analysis: for sensory analysis.

American Society for Testing and Materials, Philadelphia, P.A., U.S.A.

BIBLIOGRAPHY 207

Iiyama, S., Yahiro, M., and Toko, K. (2000). Measurements of soy sauce

using taste sensor. Sensors and Actuators B, 66:205–206.

Innawong, B., P., M., Irudayaraj, J., and Marcy, J. (2004). The determina-

tion of frying oil quality using fourier transform infrared attenuated total

reflectance. Lebensmittel-Wissenschaft und -Technologie, 37:23–28.

Inon, F., Garrigues, J., Garrigues, S., Molina, A., and de la Guardia, M.

(2003). Selection of calibration set samples in determination of olive oil

acidity by partial least squares-attenuated total reflectance-fourier trans-

form infrared spectroscopy. Analytica chimica Acta, 489:59–75.

Irudayaraj, J. and Tewari, J. (2003). Simultaneous monitoring of organic

acids and sugars in fresh and processed apple juice by fourier transform

infrared-attenuated total reflection spectroscopy. Applied Spectroscopy,

57:1599–1604.

Irudayaraj, J. and Yang, H. (2002). Depth profiling of a heterogenous

food-packaging model using fourier transform infrared photoacoustic spec-

troscopy. Journal of Food Engineering, 55:25–33.

ISO (1988). Sensory analysis. General guidance for the design of test rooms.

International Standards Organization (ISO 8589), Switzerland.

Ivarsson, P., Kikkawa, Y., Winquist, F., Krantz-Rulcker, C., Hojer, N.,

Hayashi, K., Toko, K., and Lundstrom, I. (2001). Comparison of a voltam-

metric electronic tongue and a lipid membrane taste sensor. Analytica

Chimica Acta, 449:59–68.

Johnson, R. and Wichern, D. (1992). Applied multivariate statistical analy-

sis. Third edition. Prentice Hall Inc., New Jersey, N.J., U.S.A.

Jones, R. and Scott, S. (1983). Improvement of tomato flavor by genetically

increasing sugar and acid contents. Euphytica, 32:845–855.

Jongen, W. (2002). Fruit and Vegetable Processing - Improving Quality.

Woodhead Publishing, Cambridge, U.K.

208 BIBLIOGRAPHY

Kazakevich, Y. and Lobrutto, R. (2007). HPLC for pharmaceutical scien-

tists. Wiley, New York, N.Y., U.S.A.

Kellner, R., Lendl, B., Wells, I., and Worsfold, P. (1997). Comparison of

univariate and multivariate strategies for the determination of sucrose in

fruit juices by automated flow injection analysis with fourier transform

infrared detection. Applied Spectroscopy, 51:227–235.

Kelly, J. and Downey, G. (2005). Detection of sugar adulterants in ap-

ple juice using fourier transform infrared spectroscopy and chemometrics.

Journal of Agricultural and Food Chemistry, 53:3281–3286.

Khanmohammadi, M., Karimi, M., Ghasemi, K., Jabbari, M., and Gar-

marudi, A. (2007). Quantitative determination of malathion in pesticide

by modified attenuated total reflectance-fourier transform infrared spec-

trometry applying genetic algorithm wavelenght selection method. Ta-

lanta, 72:620–625.

Kikkawa, Y., Toko, K., and Yamafuji, K. (1993). Taste sensing of tomatoes

with a multichannel taste sensor. Sensing Materials, 5:83–90.

Lachenmeier, D. (2007). Rapid quality control of spirit drinks and beer

using multivariate data analysis of fourier transform infrared spectra. Food

Chemistry, 101:825–832.

Lagaron, J., Fernandez-Saiz, P., and Ocio, M. (2007). Using atr-ftir spec-

troscopy to design active antimicrobial food packaging structures based

on high molecular weight chitosan polysaccharide. Journal of Agricultural

and Food Chemistry, 55:2554–2562.

Lankmayr, E., Mocak, J., Serdt, K., Balla, B., Wenzl, T., Bandoniene,

D., Gfrerer, M., and Wagner, S. (2004). Chemometrical classification of

pumpkin seed oils using uv-vis, nir and ftir spectra. Journal of biochemical

and biophysical methods, 61:95–106.

Le Thanh, H. and Lendl, B. (2000). Sequential injection fourier transform

infrared spectroscopy for the simultaneous determination of organic acids

BIBLIOGRAPHY 209

and sugars in soft drinks employing automated solid phase extraction.

Analytica Chimica Acta, 422:63–69.

Legin, A. (2007). Personal communication.

Legin, A., Kirsanov, D., Rudnitskaya, A., Beullens, K., Lammertyn, J.,

Nicolaı, B., Irudayaraj, J., and Vlasov, Y. (2005a). Analysis of apple va-

rieties - comparison of et with different analytical techniques. Proceedings

of ISOEN, International Symposium on Olfaction and Electronic Nose,

Barcelona, Spain, pages 176–177.

Legin, A., Lvova, L., Rudnitskaya, A., Vlasov, Y., Di Natale, C., and

D’Amico, A. (2003). Evaluation of italian wine by the electronic tongue:

recognition, quantitative analysis and correlation with human sensory per-

ception. Analytica Chimica Acta, 484:33–44.

Legin, A., Rudnitskaya, A., Seleznev, B., and Vlasov, Y. (2002a). Recog-

nition of liquid and flesh food using an ’electronic tongue’. International

Journal of Food Science and Technology, 37:375–385.

Legin, A., Rudnitskaya, A., Seleznev, B., and Vlasov, Y. (2005b). Electronic

tongue for quality assessment of ethanol, vodka and eau-de-vie. Analytica


Legin, A., Rudnitskaya, A., and Vlasov, Y. (2002b). Electronic tongues:

sensors, systems, applications. Sensors Update, 10:143–188.

Legin, A., Rudnitskaya, A., Vlasov, Y., Di Natale, C., and D’Amico, A.

(1999a). The features of the electronic tongue in comparison with the

characteristics of the discrete ion-selective sensors. Sensors and Actuators

B, 58:464–468.

Legin, A., Rudnitskaya, A., Vlasov, Y., Di Natale, C., Davide, F., and

D’Amico, A. (1997). Tasting of beverages using an electronic tongue.

Sensors and Actuators B, 44:291–296.

Legin, A., Rudnitskaya, A., Vlasov, Y., Di Natale, C., Mazzone, E., and

D’Amico, A. (1999b). Application of electronic tongue for quantitative

analysis of mineral water and wine. Electroanalysis, 11:814–820.

210 BIBLIOGRAPHY

Legin, A., Rudnitskaya, A., Vlasov, Y., Di Natale, C., Mazzone, E., and

D’Amico, A. (2000). Application of electronic tongue for qualitative and

quantitative analysis of complex liquid media. Sensors and Actuators B,

65:232–234.

Lendl, B. and Kellner, R. (1995). Determination of sucrose by flow injection

analysis with fourier transform infrared detection. Mikrochimica Acta,

119:73–79.

Lendl, B. and Schindler, R. (1999). Flow-through sensors for enhancing sen-

sitivity and selectivity of ftir spectroscopy in aqueous media. Vibrational

Spectroscopy, 19:1–10.

Lenehan, C., Barnett, N., and Lewis, S. (2002). Sequential injection analysis.

Analyst, 127:997–1020.

Liu, H. and Webster, T. (2007). Nanomedicine for implants: a review of

studies and necessary experimental tools. Biomaterials, 28:354–369.

Lvova, L., Legin, A., Vlasov, Y., Cha, G., and Nam, H. (2003). Multicom-

ponent analysis of korean green tea by means of disposable all-solid-state

potentiometric electronic tongue microsystem. Sensors and Actuators B,

95:391–399.

MacBride, D., Malone, C., Hebb, J., and Cravalho, E. (1997). Effect of tem-

perature variation on ft-ir spectrometer stability. Applied Spectroscopy,

51:43–50.

Malundo, T., Shewfelt, R., and Scott, J. (1995). Flavor quality of fresh

tomato (Lycopersicon esculentum mill.) as affected by sugar and acid lev-

els. Postharvest Biology and Technology, 6:103–110.

Martens, H. and Naes, T. (1998). Multivariate Calibration. John Wiley and

Sons Ltd., Chichester, UK.

Martens, H. and Stark, E. (1991). Extended multiplicative signal correc-

tion and spectral interference subtraction-new preprocessing methods for

near-infrared spectroscopy. Journal of Pharmaceutical and Biomedical

Analysis, 9:625–635.

BIBLIOGRAPHY 211

Meilgaard, M., Civille, G., and Carr, B. (2007). Sensory evaluation tech-

niques, Fourth edition. CRC Press, Boca Raton, Florida, U.S.A.

Meyerhof, W., Behrens, M., Brockhoff, A., Bufe, B., and Kuhn, C. (2005).

Human bitter taste perception. Chemical Senses, 30:i14–i15.

Mifsud, J. and Lucas, Q. (2003). Apparatus and method for characterizing

liquids - us patent 6290838.

Molnar-Perl, I. (1999). Simultaneous quantitation of acids and sugars by

chromatography: gas or high-performance liquid chromatography? Jour-

nal of Chromatography A, 845:181–195.

Moreira, J. and Santos, L. (2004). Spectroscopic interferences in fourier

transform infrared wine analysis. Analytica chimica Acta, 513:263–268.

Moros, J., Inon, F., Garrigues, S., and de la Guardia, M. (2005). De-

termination of the energetic value of fruit and milk-based beverages

through partial-least-squares attenuated total reflectance-fourier trans-

form infrared spectroscopy. Analytica chimica Acta, 538:181–193.

Naes, T., Isaksson, T., Fearn, T., and Davies, T. (2004). A User-friendly

Guide to Multivariate Calibration and Classification. NIR publications,

Charlton, Chichester, UK.

Ninomiya, K. (2002). Umami: a universal taste. Food Reviews International,

18:23–38.

NIST/SEMATECH (07/2007). NIST/SEMATECH e-Handbook

of Statistical Methods. http://www.itl.nist.gov/div898/ hand-

book/pri/section3/pri3361.htm.

Noble, A. (1996). Taste-aroma interactions. Trends in Food Science and


Nollet, L. (1992). Food Analysis by HPLC. Marcel Dekker, New York, N.Y.,

U.S.A.

212 BIBLIOGRAPHY

Paradkar, M. and Irudayaraj, J. (2002). Rapid determination of caffeine

content in soft drinks using ftir-atr spectroscopy. Food Chemistry, 78:261–

266.

Pearce, T., Schiffman, S., Nagle, H., and Gardner, J. (2003). Handbook of

Machine Olfaction: Electronic Nose Technology. Wiley, New York, N.Y.,

U.S.A.

Perez-Olmos, R., Soto, J., Zarate, N., Araujo, A., and Montenegro, M.

(2005). Sequential injection analysis using electrochemical detection: a

review. Analytica Chimica Acta, 554:1–16.

Peris-Vicente, J., Lerma-Garcia, M., Simo-Alfonso, E., Gimento-

Adelantado, J., and Domenech-Carbo, M. (2007). Use of linear discrim-

inant analysis applied to vibrational spectroscopy data to characterize

commercial varnishes employed for art purposes. Analytica Chimica Acta,

589:208–215.

Petersen, K., Willumsen, J., and Kaack, K. (1998). Composition and taste

of tomatoes as affected by increased salinity and different salinity sources.

Journal of Horticultural Science and Biotechnology, 73:205–215.

Petro-Truza, M. (1987). Flavor of tomato and tomato products. Food Re-

views International, 2:309–351.

Piggott, J., Simpson, S., and Williams, S. (1998). Sensory analysis. Inter-

national Journal of Food Science and Technology, 33:179–183.

Ramos Da Conceicao Neta, E., Johanningsmeier, S., and McFeeters, R.

(2007). The chemistry and physiology of sour taste a review. Journal of

Food Science, 72:33–38.

Rosenberg, E. and Kellner, R. (1994). Flow injection analysis with fourier

transform detection for clinical and process analysis. Fresenius Journal

of Analytical Chemistry, 348:530–532.

Roychoudhury, P., Harvey, L., and McNeil, B. (2006). At-line monitoring of

ammonium, glucose, methyl oleate and biomass in a complex antibiotic

BIBLIOGRAPHY 213

fermentation process using attenuated total reflectance-mid-infrared (atr-

mir) spectroscopy. Analytica Chimica Acta, 561:218–224.

Rudnitskaya, A., Kirsanov, D., Legin, A., Beullens, K., Lammertyn, J.,

Nicolaı, B., and Irudayaraj, J. (2006). Analysis of apple varieties - com-

parison of electronic tongue with different analytical techniques. Sensors

and Actuators B, 116:23–28.

Rudnitskaya, A., Legin, A., Makarychev-Mikhailov, S., Goryacheva, O., and

Vlasov, Y. (2001). Quality monitoring of fruit juices using an electronic

tongue. Analytical Sciences, 17:i309.

Ruzicka, J. (2000). Lab-on-valve: universal microflow analyzer based on

sequential and bead injection. Analyst, 125:1053–1060.

Ruzicka, J. and Hansen, E. (1975). Flow injection analysis. part 1. a nex

concept. Analytica Chimica Acta, 78:145–157.

Ruzicka, J. and Marshall, G. (1990). Sequential injection: a new concept

for chemical sensors, process analysis and laboratory assays. Analytica


Saeys, W., Beullens, K., Lammertyn, J., Ramon, H., and Naes, T. (2008).

Increasing robustness against changes in the interferent structure by incor-

porating prior information in the augmented classical least squares (acls)

framework. Analytical Chemistry, Submitted.

Saeys, W., Mouazen, A., and Ramon, H. (2005). Potential for onsite and

online analysis of pig manure using visible and near infrared spectroscopy.

Biosystems Engineering, 91:393–402.

Salles, C., Nicklaus, S., and C., S. (2003). Determination and gustatory

properties of taste-active compounds in tomato juice. Food Chemistry,

81:395–402.

SAS (12/2007). SAS Manual SAS/STAT.

http://www2.stat.unibo.it/ManualiSas/stat/chap56.pdf.

214 BIBLIOGRAPHY

Schindler, R. and Lendl, B. (1999). Ftir spectroscopy as detection principle

in aqueous flow analysis. Analyical Communications, 36:123–126.

Schindler, R., Vonach, R., Lendl, B., and Kellner, R. (1998a). A rapid

automated method for wine analysis based upon sequential injection (si)-

ftir spectrometry. Fresenius Journal of Analytical Chemistry, 362:130–

136.

Schindler, R., Watkins, M., Vonach, R., Lendl, B., Kellner, R., and Sara,

R. (1998b). Automated multivariate calibration in sequential injection-

fourier transform infrared spectroscopy for sugar analysis. Analytical

Chemistry, 70:226–231.

Sharma, S. (1996). Applied multivariate techniques. John Wiley and Sons

Inc., Hoboken, N.J., U.S.A.

Smith, B. (1996). Fundamentals of Fourier Transform Infrared Spectroscopy.

CRC Press, Boca Raton, Florida, U.S.A.

Smith, D. and van der Klaauw, N. (1995). The perception of saltiness is

eliminated by nacl adaptation: implications for gustatory transduction

and coding. Chemical Senses, 20:545–557.

Stahl, W. (1973). Compilation of odor and taste threshold values data.

ASTM Data Ser DS 48. American Society for Testing and Materials,

Philadelphia, P.A., U.S.A.

Stevens, M., Kader, A., Albright-Holton, M., and Algazi, M. (1977). Geno-

typic variation for flavor and composition in fresh market tomatoes. Jour-

nal of the American Society for Horticultural Science, 102:680–689.

Stevenson, R., Prescott, J., and Boakes, R. (1999). Confusing tasted and

smells: how odours can influence the perception of sweet and sour tastes.

Chemical Senses, 24:627–635.

Takagi, S., Toko, K., Wada, K., and Ohki, T. (2001). Quantification of

suppression of bitterness using an electronic tongue. Journal of Pharma-

ceutical Sciences, 90:2042–2048.

BIBLIOGRAPHY 215

Tan, T., Schmitt, V., and Isz, S. (2001). Electronic tongue: a new dimension

in sensory analysis. Foodtechnology, 55:44–50.

Teranishi, R., Wick, E., and Hornstein, I. (1999). Flavor chemistry. Kluwer

Academic/Plenum Publishers, New York, N.Y., U.S.A.

Toko, K. (1996). Taste sensor with global selectivity: Review. Materials

Science and Engineering, C4:69–82.

Toko, K. (1998a). Electronic sensing of taste. Electroanalysis, 10:657–669.

Toko, K. (1998b). Electronic tongue. Biosensors and Bioelectronics, 13:701–

709.

Toko, K. (2000a). Biomimetic sensor technology. Cambridge University

Press, Cambridge, U.K.

Toko, K. (2000b). Taste sensor. Sensors and Actuators B, 64:205–215.

Tran, T., Suzuki, K., Okadome, H., Homma, S., and Ohtsubo, K. (2004).

Analysis of the tastes of brown rice and milled rice with milling yields

using a taste sensing system. Food Chemistry, 88:557–566.

Trygg, J. and Wold, S. (2002). Orthogonalized projections to latent struc-

tures (o-pls). Journal of Chemometry, 16:119–128.

USDA (1975). Colour classification requirements in tomatoes, USDA Visual

Aid TM-L-1. The John Henry Co., Lansing, Mich., U.S.A.

van Staden, J. and Stefan, R. (2004). Chemical separation by sequential

injection analysis: an overview. Talanta, 64:1109–1113.

VBT (2006). Jaarverslag 2005 van het VBT.

Velterop, J. and Vos, F. (2001). A rapid and inexpensive microplate assay

for the enzymatic determination of glucose, fructose, sucrose, l-malate and

citrate in tomato (Lycopersicon esculentum) extracts and in orange juice.

Phytochemical Analysis, 12:299–304.

216 BIBLIOGRAPHY

Venyaminov, S. and Prendergast, F. (1997). Water (h2o and d2o) molar

absorptivity in the 1000-4000 cm−1 range and quantitative infrared spec-

troscopy of aqueous solutions. Analytical Biochemistry, 248:234–245.

Vermeir, S., Nicolaı, B., Jans, K., Maes, G., and Lammertyn, J. (2007).

High-throughput microplate enzymatic assays for fast sugar and acid

quantification in apple and tomato. Journal of Agricultural and Food

Chemistry, 55:3240–3248.

Vlasov, Y. and Bychkov, Y. (1995). Chalcogenide ion selective electrodes -

us patent 5464511. 90.

Vlasov, Y., Legin, A., and Rudnitskaya, A. (2002). Electronic tongues

and their analytical application. Analytical and Bioanalytical Chemistry,

373:136–146.

Vlasov, Y., Legin, A., Rudnitskaya, A., D’Amico, A., and C., D. N. (2000).

Electronic tongue - new analytical tool for liquid analysis on the basis

of non-specific sensors and methods of pattern recognition. Sensors and

Actuators B, 65:235–236.

Vonach, R., Lendl, B., and Kellner, R. (1998). High-performance liquid

chromatography with real-time fourier-transform infrared detection for

the determination of carbohydrates, alcohols and organic acids in wines.

Journal of Chromatography A, 824:159–167.

Wang, J. (2006). Analytical electrochemistry, 3rd edition. Wiley, New York,

N.Y., U.S.A.

Wang, J.-H. and Hansen, E. (2003). Sequential injection lab-on-valve: the

third generation of flow analysis. Trends in Analytical Chemistry, 22:225–

231.

Willard, H., Merritt, L., Dean, J., and Settle Jr., F. (1988). Instrumen-

tal Methods of Analysis, 7th ed. Wadsworth Publishing Co., Belmont,

California, U.S.A.

Wilson, R. and Goodfellow, B. (1994). Spectroscopic techniques for food

analysis. VCH Publishers, New York, N.Y., U.S.A.

BIBLIOGRAPHY 217

Winquist, F., Bjorklund, R., Krantz-Rulcker, C., Lundstrom, I., Ostergren,

K., and Skoglund, T. (2005). An electronic tongue in the dairy industry.

Sensors and Actuators B, 111–112:299–304.

Winquist, F., Krantz-Rulcker, C., Wide, P., and Lundstrom, I. (1998). Mon-

itoring of freshness of milk by an electronic tongue on the basis of voltam-

metry. Measurement Science and Technology, 9:1937–1946.

Winquist, F., Krantz-Rulcker, C., Wide, P., and Lundstrom, I. (2000). A

hybrid electronic tongue. Analytica Chimica Acta, 406:147–157.

Winquist, F., Wide, P., and Lundstrom, I. (1997). An electronic tongue

based on voltammetry. Analytica Chimica Acta, 375:21–31.

Xu, Y., Zhao, Y., Ling, X., Yang, L., Xu, Z., Zhou, X., Zhang, Y., Gong, R.,

Pan, Q., Wang, B., Shi, J., Xu, D., and Wu, J. (2007). Fourier transform

mid-infrared spectroscopy (ftir) used for the rapid intraoperative diagnosis

of gallbladder diseases. Chemical Journal of Chinese Universities, 28:645–

648.

Yamanaka, H., Chachin, K., and Ogata, K. (1971). Studies on the

metabolism of free amino acids during maturation and ripening of tomato

fruits. 2. changes of the activities of glutamic acid decarboxylase and glu-

tamic acid dehydrogenase in tomato fruits during maturation and ripen-

ing. Journal of the Japanese Society for Horticulural Science, 40:287–291.

218 BIBLIOGRAPHY

List of publications

International journal publications

Beullens, K., Buysens, S., Cap, N., Meszaros, P., Kirsanov, D., Legin, A.,

Nicolaı, B. M. and Lammertyn, J. (2008). Relating sensory analysis to

electronic tongues and ATR-FTIR for tomato taste profiling. Journal

of Agricultural and Food Chemistry, Submitted.

Beullens, K., Kirsanov, D., Irudayaraj, J., Rudnitskaya, A., Legin, A.,

Nicolaı, B. M. and Lammertyn, J. (2006). The electronic tongue and

ATR-FTIR for rapid detection of sugars and acids in tomatoes. Sen-

sors and Actuators B, 116, 107−115.

Beullens, K., Meszaros, P., Kirsanov, D., Legin, A., Buysens, S., Cap,

N., Vermeir, S., Nicolaı, B. M. and Lammertyn, J. (2008). Analysis

of tomato taste using two types of electronic tongues. Sensors and

Actuators B, 131, 10−17.

Beullens, K., Meszaros, P., Vermeir, S., Buysens, S., Cap, N., Nicolaı, B.

M. and Lammertyn, J. (2008). Development of a sequential injection

ATR-FTIR system to analyze Belgian tomato cultivars. Analytical

and Bioanalytical Chemistry, Submitted.

Nicolaı, B. M., Beullens, K., Bobelyn, E., Peirs, A., Theron, K.I. and

Lammertyn, J. (2007). Nondestructive measurement of fruit and veg-

etable quality by means of NIR spectroscopy: a review. Postharvest

Biology and Technology, 46, 99−118.

219

220

Roth, E., Berna, A.Z., Beullens, K., Yarramraju, S., Lammertyn, J.,

Schenk, A. and Nicolaı, B. M. (2007). Postharvest quality of inte-

grated and organically produced apple fruit. Postharvest Biology and

Technology, 45, 11−19.

Rudnitskaya, A., Kirsanov, D., Legin, A., Beullens, K., Lammertyn, J.,

Nicolaı, B. M. and Irudayaraj, J. (2006). Analysis of apples varieties

- comparison of electronic tongue with different analytical techniques.

Sensors and Actuators B, 116, 23−28.

Saeys, W., Beullens, K., Lammertyn, J., Naes, T. (2008). Increasing ro-

bustness against changes in the interferent structure by incorporating

prior information in the Augmented Classical Least Squares (ACLS)

framework. Analytical Chemistry, 2008, Accepted.

Vermeir, S., Hertog, M.L.A.T.M., Schenk, A., Beullens, K., Nicolaı,

B.M. and Lammertyn, J. (2008). Evaluation and optimization of high-

throughput enzymatic assays for fast L-ascorbic acid quantification in

fruit and vegetables. Analytica Chimica Acta, Submitted.

National journal publications

Berna, A., Beullens, K., Schenk, A. (2004). Effect van de steel op het

aroma van tomaten. Proeftuinnieuws, 13, 19−20.

Book chapters

Nicolaı, B. M., Berna, A., Beullens, K., Vermeir, S., Saevels, S. and

Lammertyn, J. (2008). High throughput flavour profiling of fruit. In:

Eds. Bruckner, B. and Wyllie, S.G., Fruit and Vegetable Flavour,

CRC Press-Woodland Publishing Limited, Cambridge, UK. In press.


Conference proceedings

Beullens, K., Irudayaraj, J., Nicolaı, B.M., Kirsanov, D., Legin, A. and

Lammertyn, J. (2004). Novel techniques for fast taste profiling of

tomatoes. In: Communications in Agricultural and Applied Biological

Sciences, 10th PhD Symposium on Applied biological Sciences, Gent,

Belgium. 57−60.

Beullens, K., Irudayaraj, J., Kirsanov, D., Legin, A., Nicolaı, B.M. and

Lammertyn, J. (2005). The electronic tongue and FTIR-ATR as fast

techniques for taste profiling. In: Proceedings of ISOEN, 9th Interna-

tional Symposium on Olfaction and Electronic Nose, Barcelona, Spain.

312−315.

Beullens, K., Irudayaraj, J., Kirsanov, D., Legin, A., Nicolaı, B.M. and

Lammertyn, J. (2005). The electronic tongue and FTIR for rapid taste

profiling in food. In: KVCV Voedselchemie in Vlaanderen - Trends in

Levensmiddelenanalyse, Gent, Belgium. 89−90.

Beullens, K., Sels, B.F., Schoonheydt, R.A., Nicolaı, B.M. and Lammer-

tyn, J. (2005). An optical tongue based on ATR-FTIR spectroscopy to

taste tomatoes. In: Communications in Agricultural and Applied Bio-

logical Sciences, 11th PhD Symposium on Applied biological Sciences,

Leuven, Belgium. 61−64.

Beullens, K., Sels, B.F., Schoonheydt, R.A., Nicolaı, B.M., Lammer-

tyn, J. (2006). Determination of sugars and organic acids in tomato

by means of flow injection ATR-FTIR and EMSC. In: Conference

proceedings, 10th International Conference on Flow Analysis, Porto,

Portugal. 240.

Beullens, K., Meszaros, P., Kirsanov, D., Legin, A., Nicolaı, B.M. and

Lammertyn, J. (2007). Analysis of tomato taste using two types of

electronic tongues. In: Proceedings of ISOEN, 10th International

Symposium on Olfaction and Electronic Nose, Saint-Petersburg, Rus-

sia. 146−147.

222

Beullens, K., Meszaros, P., Kirsanov, D., Legin, A., Nicolaı, B.M. and

Lammertyn, J. (2007). Taste analysis of tomato juices using two dif-

ferent multisensor systems. In: Book of Abstracts, Euroanalysis X,

Antwerp, Belgium. 591.

Beullens, K., Vermeir, S., Nicolaı, B.M. and Lammertyn, J. (2007).

ATR-FTIR as a rapid technique for taste profiling of fruit juices. In:

Book of Abstracts, Euroanalysis X, Antwerp, Belgium. 436.

Legin, A., Kirsanov, D., Rudnitskaya, A., Beullens, K., Lammertyn, J.,

Nicolaı, B., Irudayaraj, J. and Vlasov, Y. (2005). Analysis of ap-

ple varieties - comparison of ET with different analytical techniques.

In: Proceedings of ISOEN, International Symposium on Olfaction and

Electronic Nose, Barcelona, Spain. 176−177.

Nicolaı, B. M., Beullens, K., Bobelyn, E., Hertog, M. L. A. T. M.,

Schenk, A., Vermeir, S. and Lammertyn, J. (2006). Systems to char-

acterise internal quality of fruit and vegetables. Invited lecture. In:

Purvis, A. C., McGlasson, W. B., Kanlayanarat, S. (Eds.), Acta Hor-

ticulturae : Proceedings of the Fourth International Conference on

Managing Quality in Chains. ISHS, Leuven , Belgium . 59−66.

Roth, E., Berna, A. Z., Beullens, K., Franck, C., Lammertyn, J., Schenk,

A. and Nicolaı, B. M. (2005). A comparative study of quality at-

tributes of integrated and organically produced apple fruit. In: 7th

Fruit, Nut and Vegetable Production Engineering Symposium: Infor-

mation and technology for sustainable fruit and vegetable production.

Roth, E., Berna, A. Z., Beullens, K., Schenk, A., Lammertyn, J. and

Nicolaı, B. M. (2005). Comparison of taste and aroma of integrated

and organic apple fruit. In: Communications in Agricultural and Ap-

plied Biological Sciences, 11th PhD Symposium on Applied biological

Sciences, Leuven, Belgium. 225−229.

Vermeir, S., Hertog, M.L.A.T.M., Schenk, A., Beullens, K., Nicolaı,

B.M. and Lammertyn, J. (2008). Evaluation and Optimization of


High-Throughput Enzymatic Assays for Fast L-Ascorbic Acid Quan-

tification in Horticultural Products. In: The 10th World Congress on

Biosensors, Shangai, China. Accepted.

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