dissertationes de agricultura model-based design of - Lirias

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Katholieke Universiteit Leuven Faculteit Bio-ingenieurswetenschappen DISSERTATIONES DE AGRICULTURA Doctoraatsproefschrift Nr. 718 aan de Faculteit Bio-ingenieurswetenschappen van de K.U.Leuven MODEL-BASED DESIGN OF SUPERATMOSPHERIC OXYGEN MODIFIED ATMOSPHERE PACKAGES Promotoren: Prof. B. Nicola¨ ı, K.U.Leuven Prof. C. Michiels, K.U.Leuven Prof. J. Van Impe, K.U.Leuven Leden van de jury: Prof. G. Volckaert, voorzitter Prof. J. De Baerdemaeker, K.U.Leuven Prof. A. Geeraerd, K.U.Leuven Prof. M. Hendrickx, K.U.Leuven Prof. R. Beaudry, Michigan State University, USA Prof. F. Devlieghere, UGent Dr. Ir. B. Verlinden, Vlaams Centrum voor Bewaring van Tuinbouwprodukten . Proefschrift voorgedragen tot het behalen van de graad van Doctor in de Bio-ingenieurswetenschappen door Sabine GEYSEN OKTOBER 2006

Transcript of dissertationes de agricultura model-based design of - Lirias

Katholieke Universiteit Leuven

Faculteit Bio-ingenieurswetenschappen

DISSERTATIONES DE AGRICULTURA

Doctoraatsproefschrift Nr. 718 aan de Faculteit

Bio-ingenieurswetenschappen van de K.U.Leuven

MODEL-BASED DESIGN OFSUPERATMOSPHERIC OXYGEN MODIFIED

ATMOSPHERE PACKAGES

Promotoren:

Prof. B. Nicolaı, K.U.Leuven

Prof. C. Michiels, K.U.Leuven

Prof. J. Van Impe, K.U.Leuven

Leden van de jury:

Prof. G. Volckaert, voorzitter

Prof. J. De Baerdemaeker, K.U.Leuven

Prof. A. Geeraerd, K.U.Leuven

Prof. M. Hendrickx, K.U.Leuven

Prof. R. Beaudry, Michigan State

University, USA

Prof. F. Devlieghere, UGent

Dr. Ir. B. Verlinden, Vlaams Centrum voor

Bewaring van Tuinbouwprodukten

.

Proefschrift voorgedragen tot

het behalen van de graad van

Doctor in de

Bio-ingenieurswetenschappen

door

Sabine GEYSEN

OKTOBER 2006

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Voorwoord

Na een periode van stoelzitten voor het schrijven, aanpassen, herschrijven en tot

slot verbeteren van een doctoraatstekst is het een verademing om - hoewel nog

steeds stoelzittend - een dankwoordje te kunnen schrijven. Niet alleen omdat

dit laatste stukje tekst het einde inluidt van de laatste doctoraatsloodjes, maar

vooral voor het plezier van het bedanken zelf.

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

dankzij jou kreeg ik de kans verder te werken in het wetenschappelijk onderzoek

en heb ik de laatste jaren enorm veel bijgeleerd. Het was (is) een plezier om in je

groep te werken. Je hebt me steeds gesteund en vertrouwen gegeven. Bedankt

voor de stimulans mijn werk te publiceren en voor te stellen op buitenlandse

congressen. Verder wil ik mijn twee andere promotoren, Prof. Chris Michiels

en Prof. Jan Van Impe bedanken om me te gidsen in de wondere wereld van de

microbiologie. Chris en Jan, ik was steeds welkom voor het uitvoeren van exper-

imenten in jullie laboratoria en voor het bespreken van resultaten. My gratitude

also goes to the members of the jury, Prof. Beaudry, Prof. Devlieghere, Prof.

Debaerdemaeker, Prof. Hendrickx, Prof. Geeraerd and Dr. Ir. Verlinden for

the critical review of the doctoral text. Prof. Guido Volckaert wil ik danken

voor het waarnemen van het voorzitterschap.

Het merendeel van de opgedane kennis in de afgelopen jaren, heb ik te danken

aan mijn begeleider Bert Verlinden. Bert, van meer praktische zaken zoals sol-

deren en proefopstellingen knutselen tot statistiek, het opstellen van modellen en

programmeren in Matlab: je hebt het me met veel geduld bijgebracht. Boven-

dien deelden we de laatste jaren een bureau en viel ik je te pas en te onpas lastig

met kleine of grote vragen. Bedankt voor die talloze keren dat je je eigen werk

opzij schoof om me verder te helpen. Ook Annemie Geeraerd zou ik van harte

willen bedanken. Annemie, bedankt voor de vanzelfsprekendheid waarmee je

tijd voor me vrijmaakte om me met veel zin voor precisie wegwijs te maken in

Baranyi- en andere curves.

Bedankt Carine, Sofie, Bert, Elfie en Jurgen voor de vele praktische hulp die

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ik van jullie kreeg en voor de goeie sfeer tijdens het werk in het labo. Verkleumd

door het urenlang snijden en wassen van sla bij 7 graden of oververhit tussen

bunzenbranders en kokend medium, het was altijd fijn om met jullie samen te

werken. Dankzij jullie werkkracht kwamen mijn soms groots opgezette experi-

menten steeds tot een goed einde. Jeroen Tirry, dankzij jou duurden compu-

terproblemen nooit lang en stonden koelcellen steeds op het gevraagde tempe-

ratuurtje. Josee en Katty, bij jullie kon ik steeds terecht voor het regelen van

administratie en andere praktische zaken. Bedankt.

Bram Aertsen wil ik bedanken voor het aanmaken van de GFP-fluorescente

Pseudomonas stam en de mensen van het Centrum voor Microbiele en Planten-

genetica voor het beschikbaar stellen van het Lightools Systeem om deze gloeien-

de beestjes te visualiseren. Also thanks to Romina and Elfie to help me out with

some protein work.

I wish to thank Rogier van der Slikke (Amcor Flexibles) and Nicolas Jaouen

(Compagnie Franco Suisse) for providing (information on) the package films.

I had the great opportunity to work together with four spanish-speaking

researchers. Victor, Perla, Violeta and Andres, you gathered a lot of data used

in this thesis. I enjoyed the time working together with you on this project.

Many thanks also for the pleasant talks and the nice times spent on conferences.

Also thanks to my thesis student Collins for carrying out some of the Listeria

experiments.

Dit doctoraatswerk was niet mogelijk geweest zonder de financiele steun van

het IWT-Vlaanderen. Ik wens de leden van de gebruikerscommissie van het

CO-020803 project te bedanken voor hun opbouwende kritiek.

Tijdens de voorbije jaren heb ik veel collega’s gehad waar ik veel aan te

danken heb. Tijdens mijn periode op het Fruitteeltcentum, waar ik als pas-

afgestudeerd groentje mijn loopbaan begon, heb ik enorm genoten van de plezan-

te momenten met mijn toenmalige collega’s. Bedankt (ex)-FTCers voor de vele

leutige momenten en voor de vriendschapsband die nog steeds bestaat. Natuur-

lijk wil ik ook al mijn collega’s van het VCBT en LNT bedanken, ook diegenen

die er enkel tijdens mijn beginjaren bij waren. Bedankt allemaal, voor jullie

hulpvaardigheid, de babbels over koetjes en kalfjes, de middagjes Via Via, voor

de goeie sfeer. Bedankt Elfie en Katrien omdat ik op jullie steeds kan rekenen

om te babysitten. Ook de collega’s van het Centum voor Levensmiddelen en

Microbiele Technologie en de Afdeling Chemische en Biochemische Procestech-

nologie en -regeling wil ik bedanken om een plaatsje vrij te maken in het labo,

voor de fijne babbels en de plezante tijd tijdens het congres in Quimper.

Het leven is meer dan werken alleen: daarom verdienen mijn familie, schoon-

Voorwoord v

familie en vrienden minstens ook een vermelding in dit dankwoord.

Moeke en vake, jullie wil ik dubbel en dik in de bloemetjes zetten. Wat jullie

hebben mogelijk gemaakt is veel grootser dan het behalen van een doctoraat.

Jullie waren vastbesloten al jullie kinderen de kans te geven verder te studeren.

Hoewel die mogelijkheid niet evident was met vijf kinderen en een eenvoudige

job, werden we bovendien volledig vrij gelaten in onze studiekeuze en hoe we

onze studies aanpakten. Daardoor hebben jullie niet alleen dit doctoraat mo-

gelijk gemaakt, maar wat belangrijker is: ieder van ons is terecht gekomen in

een job waarin we ons gelukkig voelen. Een dikke merci namens ons allemaal.

Bedankt Tim, voor je hulp bij het gebruik van LATEX en het verdragen van

gezeur tijdens de laatste schrijfmaanden. Maar vooral omdat je onmisbaar voor

me bent: door je aanstekelijk en oprecht enthousiasme, je steeds opbeurende

woordjes, je liefde voor mij en onze kindjes: Sander en ons nieuwe baby’tje dat

nu vollop groeit in mijn buik...

Sabine,

Oktober 2006

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Nederlandse samenvatting

Vers fruit en verse groenten zijn van groot belang in een gezonde voeding. Tem-

peratuurscontrole is de belangrijkste maatregel om de kwaliteit van verse en

versneden produkten te bewaren. Bijkomend kan hun houdbaarheid verbe-

terd worden door de omgevingsatmosfeer te wijzigen. In gewijzigde atmos-

feerverpakkingen (MAP) wordt het produkt verpakt in een verpakkingsfolie met

welbepaalde permeabiliteit. Als gevolg van produktademhaling en gasdiffusie

doorheen de folie worden in de verpakking gas condities bereikt die de houd-

baarheid kunnen verlengen en/of de kwaliteit kunnen verbeteren. Meestal wordt

voor de verpakking van groenten en fruit geopteerd voor een verlaging van de

O2-concentratie om de ademhaling te remmen en de groei van aerobe microor-

ganismen en oxidatie reacties te vertragen. In tegenstelling tot lage O2, kunnen

ook hoge O2-concentraties toegepast worden, al dan niet in combinatie met

verhoogde CO2. In dit werk wordt een model-gebaseerde aanpak voorgesteld

voor het ontwerp van hoge O2 MA verpakkingen voor twee belangrijke Belgische

tuinbouwprodukten, namelijk aardbei en versneden kropsla.

Eerst werd de ademhaling van beide produkten onderzocht bij verschillende

temperaturen, CO2- (tussen 0 en 20 kPa) en O2- (tussen 0 en 100 kPa) con-

centraties. Het remmende effect van lage temperaturen en verhoogde CO2-

concentraties op de respiratie van zowel aardbei als versneden sla, werd be-

schreven met Michaelis-Menten gebaseerde modellen. Terwijl de ademhaling

van aardbei niet beınvloed werd door hoge O2 niveau’s, werd de ademhaling

van versneden sla geremd onder deze condities, waardoor nieuwe modellen, die

het inhiberend effect van hoge O2 beschrijven, vereist waren. De dynamische

ademhalingsmodellen, gecombineerd met dynamische gasdiffusiemodellen, lieten

toe de gascondities in zowel lage als hoge O2 MA verpakkingen voor aardbei en

versneden sla te voorspellen.

Bij het ontwerpen van MA verpakkingen, is men uiteindelijk geınteresseerd

in de kwaliteit van het verpakte produkt. Daarom werd het effect van hoge O2

en CO2-concentraties op microbiele kwaliteit en op bruinverkleuring onderzocht,

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gemodelleerd en samen met de gasdiffusie- en ademhalingsmodellen ingebouwd

in een algemeen MAP model.

De bacterien Pseudomonas fluorescens en Listeria innocua werden bestu-

deerd als modelorganismen voor respectievelijk bederf- en pathogene microorga-

nismen. Het remmende effect van hoge O2 en CO2-concentraties op de groei

van P. fluorescens en van hoge CO2-concentraties op L. innocua werd gekwan-

tificeerd in predictieve groeimodellen, met name Baranyi-modellen waarin de

lagtijd en de specifieke groeisnelheid beschreven werden als functie van de gas-

conditie. Doordat versneden produkten mechanisch worden beschadigd vormt

enzymatische bruinverkleuring een probleem. De activiteit van polyfenoloxidase

(PPO), een belangrijk enzyme in het bruinverkleuringsproces werd onderzocht

in een bioreactor. Hoge O2-concentraties hadden een remmende werking op

PPO activiteit, wat deels te wijten was aan de hoge O2-concentraties zelf, en

deels aan de verhoogde produktconcentraties in de bioreactor. Beide effecten

werden beschreven met een gecombineerd Michaelis-Menten model. Wanneer

hoge O2 en CO2 atmosferen werden toegepast op versneden sla, werd de bruin-

verkleuring vertraagd. De bruinverkleuring van sla werd goed beschreven door

middel van een autokatalytisch PPO gebaseerd verbruiningsmodel, welke bruin-

verkleuring beschrijft als een PPO-gemedieerde enzymatische reactie waarbij een

inactieve precursor wordt geactiveerd tot actief PPO door het vrijkomen van

membraanvetzuren. Het remmende effect van hoge O2-concentraties op bruin-

verkleuring, kon gelinkt worden met een verminderde PPO activiteit, hetgeen

de resultaten van het bioreactor-experiment bevestigde. CO2 had op zijn beurt

een vertragend effect op veroudering en het hiermee gepaard gaande verlies in

membraanintegriteit.

Het opgestelde hoge O2 MAP model werd toegepast om optimale hoge O2

MA verpakkingen voor aardbei en versneden sla te ontwikkelen. Hiervoor wer-

den simulaties uitgevoerd met behulp van een grafische gebruikersinterface. Ver-

pakken onder hoge O2 atmosfeer bleek ongeschikt voor aardbei, hoofdzakelijk

omwille van de hoge ademhalingssnelheid waardoor in een verpakkingsfolie met

hoge permeabiliteit de hoge O2 condities slechts beperkt bewaard blijven en an-

derzijds in een verpakkingsfolie met lage permeabiliteit het gevaar voor anaerobe

condities reeel is. De toepassing van hoge O2 verpakkingen is wel beloftevol voor

de trager respirerende versneden kropsla. In hoge O2 MA verpakkingen ontwik-

kelen bruinverkleuring en Pseudomonaceae zich minder snel dan in lage O2 MA

verpakkingen.

Abstract

Fresh fruit and vegetables are important ingredients of the human diet. The

most important way to keep postharvest quality of fresh and fresh-cut produce

is stringent temperature control. Additionally, keeping quality can be improved

and/or prolonged by altering the atmosphere surrounding the product. In mod-

ified atmosphere packaging (MAP), the product is packaged in a film with suit-

able permeability characteristics. As a result of product respiration and gas

diffusion through the film, gas conditions appropriate to improve the shelf-life

and maintain the quality of the product, are obtained. Mostly, the oxygen

concentration is reduced in order to suppress respiration and retard growth of

aerobic micro-organisms and oxidation reactions. In contrast to reducing the

oxygen concentration, high oxygen levels (above the 21 kPa present in normal

air) may be applied, alone or in combination with elevated CO2 concentrations.

This thesis provides a model-based approach for the design of high oxygen MA

packages of two important Belgian horticultural products, strawberry and fresh-

cut butterhead lettuce.

First, the respiration rate of both products was investigated under a range of

temperatures, CO2 concentrations (0 to 20 kPa) and O2 concentrations (ranging

from 0 to 100 kPa). Respiration models to describe the inhibiting effect of low

temperature and elevated CO2 levels on the respiration of strawberry and fresh-

cut butterhead lettuce were built. Whereas the respiration of strawberry was

not affected by high O2 levels, the respiration of fresh-cut butterhead lettuce was

reduced at these conditions, for which novel models incorporating the inhibiting

effect of high O2 levels were required. The dynamic respiration models, together

with dynamic models to describe the diffusion of gases through the package film,

enabled to predict gas conditions in strawberry or fresh-cut lettuce low O2 as

well as high O2 MA packages.

Since eventually, a package designer is interested in the quality of the pack-

aged produce, the effect of high O2 and CO2 levels on microbial quality, micro-

bial safety and browning of fresh-cut butterhead lettuce was examined, modelled

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and incorporated in an overall MAP model together with the respiration and

diffusion models. For strawberry, only respiration models were incorporated at

this stage, but as soon as quality models (e.g. for Botrytis growth, colour and

aroma changes) become available, they should be implemented to provide more

insight on the effects on final quality.

Pseudomonas fluorescens and Listeria innocua were studied as model-organ-

isms for respectively spoilage and pathogenic micro-organisms. The inhibitive

effect of elevated CO2 and high O2 levels on P. fluorescens growth and of el-

evated CO2 on Listeria innocua growth was quantified for the first time in

predictive growth models, which were obtained by incorporating the gas effects

on lag time and maximum specific growth rate in a Baranyi-model. Addition-

ally, enzymatic browning which is of concern for fresh-cut produce due to the

mechanical damage to the tissue, was studied. Since the enzyme polyphenol

oxidase (PPO) is largely responsible for browning reactions, its activity was

studied in in vitro studies using a bioreactor. It was shown that high O2 levels

had an inhibiting effect on PPO activity, due to a combined inhibitive effect from

the high initial O2 levels and the increased reaction product concentrations in

the bioreactor. The inhibitory effects were quantified in a combined Michaelis-

Menten model that incorporated the product (oxidised chlorogenic acid) as a

competitive inhibitor and O2 as an uncompetitive inhibitor. Also when ap-

plied to fresh-cut butterhead lettuce, high O2 and CO2 gas atmospheres delayed

browning. Browning could be modelled succesfully using an autocatalytic PPO

based browning model, that described browning as a PPO-mediated enzymatic

reaction including the activation of an inactive precursor of PPO through the re-

lease of fatty acids from membranes, which occurs during senescence. Whereas

the inhibitive effect of high O2 concentrations on browning was linked with a

decreased PPO activity, which confirmed the results of the in vitro assay, CO2

mainly delayed senescence and the herewith linked loss in membrane integrity.

The constructed overall high O2 MAP model was applied to design optimal

high O2 strawberry and cut lettuce packages. Based on our simulations using a

graphical user interface, high O2 MAP showed to be unsuitable for the storage

of strawberries. Due to the fast respiration of strawberries, film permeabilities

should be quite high in order to avoid unappropriate anoxic conditions. How-

ever, when film permeabilities are too high, the desired high O2 conditions are

only kept for a short period of time. The application of high O2 MA packages

for fresh-cut butterhead lettuce looked more promising. In high O2 MA pack-

ages, browning and pseudomonads growth is significantly retarded as compared

to in low O2 MA packages.

Contents

Voorwoord iii

Nederlandse samenvatting vii

Abstract ix

Contents xi

Symbols and Abbreviations xv

1 General introduction 1

1.1 Importance of strawberry and butterhead lettuce in Belgium . . 1

1.2 Modified atmosphere packaging (MAP) of fresh produce . . . . . 2

1.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2.2 Superatmospheric oxygen MAP . . . . . . . . . . . . . . . 3

1.2.3 Quality characteristics of MA packaged produce . . . . . 4

1.3 Model-based design of MA packages . . . . . . . . . . . . . . . . 7

1.4 Objectives and outline of the thesis . . . . . . . . . . . . . . . . . 8

2 Analysis and modelling of high oxygen effects on respiration 11

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . 14

2.2.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . 14

2.2.2 Analysis of variance . . . . . . . . . . . . . . . . . . . . . 17

2.2.3 Modelling the respiration rates . . . . . . . . . . . . . . . 18

2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.3.1 Analysis of variance . . . . . . . . . . . . . . . . . . . . . 20

2.3.2 Modelling the respiration rates . . . . . . . . . . . . . . . 21

2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.4.1 Temperature effect . . . . . . . . . . . . . . . . . . . . . . 32

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2.4.2 Carbon dioxide effect . . . . . . . . . . . . . . . . . . . . 33

2.4.3 Effect of low oxygen concentrations . . . . . . . . . . . . . 34

2.4.4 Effect of high oxygen concentrations . . . . . . . . . . . . 35

2.4.5 Influence of respiration measurement method on rq . . . . 36

2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3 Analysis and modelling of high oxygen effects on microbial

growth 41

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . 43

3.2.1 Bacterial strains and inoculation of growth substrates . . 43

3.2.2 Experimental setup . . . . . . . . . . . . . . . . . . . . . . 45

3.2.3 Enumeration of viable cells . . . . . . . . . . . . . . . . . 47

3.2.4 pH measurements . . . . . . . . . . . . . . . . . . . . . . 49

3.2.5 Model development . . . . . . . . . . . . . . . . . . . . . . 49

3.2.6 Model validation . . . . . . . . . . . . . . . . . . . . . . . 53

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

3.3.1 Primary growth model . . . . . . . . . . . . . . . . . . . . 58

3.3.2 Secondary growth models . . . . . . . . . . . . . . . . . . 61

3.3.3 Overall growth models . . . . . . . . . . . . . . . . . . . . 62

3.3.4 Calculation of risk areas . . . . . . . . . . . . . . . . . . . 65

3.3.5 Model validation . . . . . . . . . . . . . . . . . . . . . . . 65

3.3.6 pH measurements . . . . . . . . . . . . . . . . . . . . . . 76

3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

3.4.1 High O2 and CO2 effects on in vitro growth . . . . . . . . 77

3.4.2 Modelling and primary model errors . . . . . . . . . . . . 80

3.4.3 Growth on fresh-cut butterhead lettuce and overall model

errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

4 Analysis and modelling of high oxygen effects on enzymatic

brown discoloration 87

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

4.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . 90

4.2.1 In vitro PPO assay . . . . . . . . . . . . . . . . . . . . . . 90

4.2.2 Brown discoloration of fresh-cut butterhead lettuce . . . . 94

4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

4.3.1 In vitro PPO activity . . . . . . . . . . . . . . . . . . . . 99

4.3.2 Brown discoloration of fresh-cut butterhead lettuce . . . . 104

Table of Contents xiii

4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

4.4.1 In vitro PPO activity . . . . . . . . . . . . . . . . . . . . 113

4.4.2 Brown discoloration of fresh-cut butterhead lettuce . . . . 114

4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

5 Overall MAP simulation model 119

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

5.2 Development of a MAP simulation model . . . . . . . . . . . . . 120

5.2.1 Model equations . . . . . . . . . . . . . . . . . . . . . . . 120

5.2.2 Development of a user interface . . . . . . . . . . . . . . . 129

5.3 Application of the MAP simulation model . . . . . . . . . . . . . 137

5.3.1 Design of packages . . . . . . . . . . . . . . . . . . . . . . 137

5.3.2 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . 143

5.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

6 General conclusions and future work 153

6.1 General conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . 153

6.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

Appendix 1 159

Appendix 2 163

Bibliography 165

List of publications 181

xiv Table of Contents

Symbols and Abbreviations

A film area (m2)

Acount accuracy factor calculated on bacterial counts

Aµ accuracy factor calculated on specific growth rates

AMC aerobic mesophilic count

aw water activity

Bcount bias factor calculated on bacterial counts

Bµ bias factor calculated on specific growth rates

BCCMTM/LMG Belgian Co-ordinated Collections of Micro-organisms/

Laboratory for Microbiology Ghent University

CA controlled atmosphere

CFC cetrimidine - fucidin - cephalosporin

cfu colony forming units

CGA chlorogenic acid

CIE Commission Internationale de l’Eclairage

CIP Culture Institut Pasteur

cv cultivar

d film thickness (m)

DCO2 CO2 diffusion coefficient (mol s−1 Pa−1 m−1)

DCO2ref CO2 diffusion coefficient at

reference temperature (mol s−1 Pa−1 m−1)

DO2 O2 diffusion coefficient (mol s−1 Pa−1 m−1)

DO2ref O2 diffusion coefficient at reference

temperature (mol s−1 Pa−1 m−1)

DP diphenols

E(t) environmental conditions

EaDO2activation energy of O2 diffusion (J mol−1)

EaVmCO2activation energy of the CO2 production rate (J mol−1)

EaVmO2activation energy of the O2 consumption rate (J mol−1)

xv

xvi

EMAP equilibrium modified atmosphere package

FA fatty acids

FR free radicals

GC gas chromatograph

GFP green fluorescent protein

k number of gas conditions

kbrown rate constant for PPO catalysed oxidation (day−1)

k′brown rate constant for PPO catalysed oxidation independent from

gas condition (day−1)

kcat reaction rate constant (mM s−1)

KiCO2 parameter describing the inhibitive effect of CO2 on ksen (%)

KiO2 parameter describing the inhibitive effect of O2 on kbrown (%)

KiQ parameter describing the inhibitive effect of quinones on kbrown

Km Michaelis-Menten rate constant (mM)

Kmc Michaelis-Menten constant for competitive inhibition (mM)

KmcCO2 Michaelis-Menten constant for competitive inhibition of CO2

(kPa)

Kmn Michaelis-Menten constant for non-competitive inhibition (mM)

KmnCO2 Michaelis-Menten constant for non-competitive inhibition of

CO2 (kPa)

KmO2 Michaelis-Menten constant for O2 (kPa)

KmO2f parameter describing the inhibition on the fermentative

metabolism by O2 (kPa)

KmO2i parameter for noncompetitive oxidative inhibition of

superatmospheric oxygen concentrations (kPa)

Kmu Michaelis-Menten constant for uncompetitive inhibition (mM)

KmuCO2 Michaelis-Menten constant for uncompetitive inhibition of

CO2 (kPa)

kPPO PPO activation rate constant (day−1)

ksen senescence reaction rate constant (day−1)

k′sen senescence reaction rate constant independent from gas

condition (day−1)

LAB lactic acid bacteria

l number of measurement points

CM cell membranes

Symbols and Abbreviations xvii

M product mass (kg)

MAP modified atmosphere packaging

MRS agar de Man, Rogosa and Sharpe agar

MSE mean squared error

n(t) logarithm of cell numbers

n0 logarithm of initial cell numbers

NB nutrient broth

nCO2 carbon dioxide molar mass (mol)

nmax logarithm of final cell numbers

nO2 oxygen molar mass (mol)

noverall logarithm of bacterial count predicted with

overall model

ODE ordinary differential equation

P product (mM)

PCA plate count agar

pCO2 CO2 partial pressure (Pa)

pCO2,i initial CO2 partial pressure (Pa)

pCO2,∞ CO2 partial pressure of the environment (Pa)

pend pressure in jar after sampling (mbar)

pO2 O2 partial pressure (Pa)

pO2,i initial O2 partial pressure (Pa)

pO2,∞ O2 partial pressure of the environment (Pa)

POD peroxidase

PPO polyphenol oxidase

PPS peptone physiological salt

pstart pressure in jar before sampling (mbar)

Q quinones

q(t) physiological state of microbial cells

Q(t) natural logarithm of the physiological state of microbial cells

R universal gas constant (8.314 J mol−1 K−1)

R2 coefficient of determination

R2adj adjusted coefficient of determination

rCO2 carbon dioxide production rate (mol kg−1 s−1)

rCO2,diff CO2 diffusion through package film (mol s−1)

xviii

rCO2f fermentative carbon dioxide production rate (mol kg−1 s−1)

rCO2,prod CO2 production (mol s−1)

RMSE root mean squared error

rO2 oxygen consumption rate (mol kg−1 s−1)

rO2,cons O2 consumption (mol s−1)

rO2,diff O2 diffusion through package film (mol s−1)

rO2,iterm for noncompetitive inhibition from superatmospheric

oxygen concentrations

rq respiratory quotient

ROS reactive oxygen species

rqox respiratory quotient at high O2 concentrations

S substrate (mM)

t time (s)

T temperature (K)

Tref reference temperature (K)

Tref,diff reference temperature for diffusion coefficients (K)

Tref,resp reference temperature for respiration (K)

v reaction rate (mM s−1)

V package volume (m3)

Vfree free volume (m3)

Vjar jar volume (m3)

Vmax maximum reaction rate (mM s−1)

VmCO2 maximum carbon dioxide production rate (mol kg−1 s−1)

VmCO2ref reference maximum carbon dioxide production rate (mol kg−1

s−1)

VmO2 maximum oxygen consumption rate (mol kg−1 s−1)

VmO2ref reference maximum oxygen consumption rate (mol kg−1 s−1)

WSSE weighted sum of squared errors

x gas condition

xCO2 measured CO2 (%)

xO2 measured O2 (%)

λ lag time (h)

µmax maximum specific growth rate (h−1)

ρ product density (kg m−3)

Chapter 1

General introduction

Fresh fruits and vegetables are important ingredients of the human diet. En-

couragement of an increased consumption of fresh produce is an important part

of nutritional policies and educational campaigns of national governments (e.g.

the Health, Food Chain Safety and Environment plan of the Belgian federal gov-

ernment) and international organisations. Fresh produce are sources of a variety

of compounds that have been found to improve human health through different

mechanisms of action, e.g. stimulation of the immune system, modulation of

detoxification enzymes, reduction of blood pressure, and antioxidant effects. In

this work, the focus lies on strawberry and fresh-cut butterhead lettuce, two

important Belgian horticultural products.

1.1 Importance of strawberry and butterhead

lettuce in Belgium

From an economical point of view, strawberry (Fragaria x ananassa Duchesne)

is the most important fruit crop in Belgium. Since strawberry is a high value

product, the value of strawberries that are supplied yearly in the Belgian auc-

tions exceeds that of apples and pears although its production in tons (42 800

tons) is much smaller. In 2004, strawberries were commercialised in the Bel-

gian auctions for a value of 96.6 million Euros (VBT, 2005). ‘Elsanta’ is the

most important strawberry cultivar in Belgium, followed by cultivars ‘Selva’ and

‘Darselect’. Strawberries are highly perishable products with a limited shelf-life.

With a supply of 150 million lettuce heads and a yield of 40 million Euros

in 2004, butterhead lettuce is the third most economically important vegetable

crop of Belgium after tomato and Belgian endive. However, due to an enlarge-

1

2 1.2 Modified atmosphere packaging (MAP) of fresh produce

ment of the market for alternative products, the butterhead lettuce production

and market share has undergone a decreasing trend over the last couple of years

(VBT, 2005). The rise of alternative lettuce products such as iceberg lettuce and

curled endive is associated with their higher suitability as ‘convenient’ fresh-cut

produce, due to a firm leaf structure and low susceptibility to enzymatic brown-

ing compared to butterhead lettuce. The mechanical and physiological fragility

of the more tender and tasteful butterhead lettuce hampers its use as a fresh-cut

vegetable (Varoquaux et al., 1996).

1.2 Modified atmosphere packaging (MAP) of

fresh produce

1.2.1 Introduction

Fresh-cut products, also called ‘minimally processed’ or ‘fourth generation’ pro-

duce, are subjected to mild processing operations as cutting, shredding, peeling

and chopping and are not subjected to any heat or freezing treatments that

would affect the original fresh character of the produce. The first way to keep

postharvest quality of fresh and fresh-cut produce is stringent temperature con-

trol. Additionally, keeping quality can be improved and/or prolonged by altering

the atmosphere surrounding the product. Modification of the atmosphere can

be achieved in different ways. The first is controlled atmosphere (CA) storage

where large amounts of fruits or vegetables are stored in cool rooms at altered

atmosphere. The altered atmosphere is kept constant during the whole storage

period by replacing the oxygen that is consumed by respiration and removing

the produced carbon dioxide. The first application of CA, about 60 years ago,

was to prolong the storage life of pome fruit (Kader et al., 1989; Phillips, 1996).

CA storage is the major storage technique for apples and pears.

A second way to apply atmosphere modification is modified atmosphere

packaging (MAP), a technique mostly applied for small batches of (fresh-cut)

fruits or vegetables for retail or the catering industry (Phillips, 1996). In MAP,

the product is packaged in a film with suitable permeability characteristics in

order to obtain equilibrium gas conditions appropriate to maintain the quality

of the product. Appropriate equilibrium gas conditions are the result of prod-

uct respiration and gas transport through the film. For produce with a low

respiration rate, suitable equilibrium conditions are only reached after a long

transition period. By flushing the package with the desired gas mixture prior

to sealing, called active packaging, the product benefits from an optimal storage

General introduction 3

atmosphere from the beginning to the end of the storage period (Phillips, 1996).

Optimum gas conditions are product-dependent and much research has been di-

rected toward their determination for a large number of fruits and vegetables

(Kader et al., 1989).

The oxygen concentration is usually reduced in order to supress respiration

and retard growth of aerobic micro-organisms and oxidation reactions. The re-

moved oxygen can be replaced with nitrogen, commonly acknowledged as an

inert gas, or with CO2. High levels of CO2 (starting from around 10%) are

known to reduce growth of aerobic and facultative anaerobic bacteria (Farber,

1991) with in decreasing order of sensitivity: Pseudomonads, Enterobacteriaceae

and lactic acid bacteria (Francis and O’Beirne, 1998). Fungi are in general less

sensitive to CO2 and typically levels starting from 35% are required for inhi-

bition of mycelial growth and sporulation (Burg, 2004). Because of their CO2

requirement, some fungi and aerobic bacteria are stimulated by low CO2 concen-

trations, and retarded or prevented when CO2 is removed from the surrounding

atmosphere (Rochwell and Highberger, 1927). Growth of five of six isolates

of Erwinia carotovora and two of six P. fluorescens isolates was completely

inhibited in broth aerated in CO2-free air (Burg, 2004). However, complete

removal of CO2 from the internal atmosphere in fruits is only possible using

hypobaric treatment (Burg, 2004). Therefore, in this work the study on CO2

effects will be focused on the application of elevated concentrations. Respiration

of fruit and vegetables is reduced by CO2 application and can result in delayed

senescence (Kader et al., 1989). However, elevated CO2 conditions are only

beneficial as long as CO2 levels are kept within certain tolerance limits, which

are commodity- and even cultivar-dependent (Kader et al., 1989). Above these

limits, CO2 can cause physiological disorders such as brown stain on lettuce and

internal browning in pome fruit (Mateos et al., 1993; Lammertyn et al., 2000).

1.2.2 Superatmospheric oxygen MAP

In contrast to reducing the oxygen concentration, high oxygen levels (above the

21 kPa present in normal air) may be applied. High oxygen, also called super-

atmospheric oxygen, MAP systems are known in the meat industry for many

decades (Jayas and Jeyamkondan, 2002). They stabilise the cherry red colour of

meat (large parts or cuts) by maintaining the myoglobin in the oxygenated form

(oxymyoglobin) (Gill, 1996). The application of high oxygen concentrations has

been proposed as an alternative to low oxygen MAP for fruits and vegetables by

Day (1996), who stated that high O2 levels are effective at inhibiting enzymatic

discoloration, preventing anaerobic fermentation reactions, and influencing aer-

4 1.2 Modified atmosphere packaging (MAP) of fresh produce

obic and anaerobic microbial growth. Jacxsens et al. (2001) found that high

O2 MAP was particularly effective for fresh-cut vegetables like grated celeriac

that are susceptible to enzymatic browning and spoilage by yeasts. In different

trials, high oxygen was found to keep the fresh taste and smell of the stored

product (Day, 1996; Kader and Ben-Yehoshua, 2000; Jacxsens et al., 2001).

According to Day (1996) the nutritional quality of fresh produce stored under

high oxygen is not affected. Storing strawberries at superatmospheric oxygen

concentrations can control decay caused by Botrytis cinerea and improve colour

retention (Wszelaki and Mitcham, 2000).

Since superatmospheric oxygen levels can accelerate combustion of all ma-

terials, special care should be taken in designing and using packaging machines

and gas-flushing systems to avoid ignition sources (BCGA, 1998).

1.2.3 Quality characteristics of MA packaged produce

Quality is a highly subjective judgement that depends on the commodity (Wills

et al., 1998). In general, quality covers general appearance, sensory quality

(texture and taste), nutritional and microbial quality.

Fresh-cut products are highly perishable products. Dehydration, enzymatic

discoloration, increased respiration and microbial growth may occur (Watada

and Qi, 1999) as a consequence of cell damage. In this section quality aspects

typically related to MA packaged fresh-cut produce are discussed.

1.2.3.1 Respiration in MA packages

After harvest, fruit and vegetables stay metabolically active which is reflected

by the respiration activity. Respiration occurs in living cells and is the ox-

idative breakdown of starch, sugars or organic acids to CO2, water and heat.

The respiration activity is product-specific and is a useful guide to the poten-

tial storage life of produce as many quality degradation reactions such as color

change and softening seem to be associated with respiration (Hertog et al., 1999,

2004). Generally speaking, products with a high respiration activity are more

perishable than products with low respiration rates (Wills et al., 1998). Respi-

ration rates of cut produce are generally higher than those of the uncut produce

(Morales-Castro et al., 1994). Low temperatures and oxygen concentrations

are known to reduce the respiration rate. Exposure to superatmospheric O2

concentrations may stimulate, have no effect, or reduce rates of respiration and

ethylene production, depending on the commodity, maturity and ripeness stage,

O2 concentration, storage time and temperature, and concentrations of CO2 and

ethylene (C2H4) present in the atmosphere (Kader and Ben-Yehoshua, 2000).

General introduction 5

When a vegetable or fruit product is packed in a modified atmosphere pack-

age, the gas atmosphere inside the package will be altered as a consequence of

the respiration of the product. Inversely, the respiration rate of the product also

depends on the gas composition inside the package, which will be changing until

a steady state is reached. Prediction of gas compositions in modified atmosphere

packages thus requires prediction of respiration behaviour under dynamic gas

conditions.

1.2.3.2 Microbial quality of MA packed fresh-cut vegetables

Besides demanding convenient and fresh produce with a good visual appearance

and sensory quality, consumers also expect foods to be free from potentially

harmful microbes. The challenge in the case of fresh-cut produce is to minimise

the processing that affects the fresh and healthy character of the produce (e.g.

heat, freezing or chemical treatments) while maintaining product safety and

stability (Ross et al., 1999). Besides the fact that no vigorous steps are under-

taken to eliminate potential microbial contaminants, the presence of cut surfaces

allows microbial infiltration and provides an increased surface area for contam-

ination and growth. Moisture and exudates on cut surfaces provide excellent

media for rapid growth of micro-organisms (Francis et al., 1999). Spoilage or-

ganisms can cause significant post-harvest losses, while growth of pathogenic

organisms implies a risk for human health (Nguyen-the and Carlin, 1994).

Mesophilic bacteria are the most numerous group contaminating fresh-cut

lettuce, with eigthy to ninety percent of this group being Gram-negative rods,

Pseudomonas, Enterobacter, or Erwinia species (Nguyen-the and Carlin, 1994)

with the pseudomonads being 5 to 10 times more numerous than the other

families. A high proportion of pseudomonads were found to have pectinolytic

activity and can be considered as an important group of spoilage organisms.

Human pathogens most frequently associated with fresh-cut vegetables are

Listeria monocytogenes, Aeromonas hydrophila, Clostridium botulinum, Esche-

richia coli 0157:H7 and Salmonella with the first three organisms being of most

concern for refrigerated produce because of their psychrotrophic nature (Francis

et al., 1999). C. botulinum only grows under conditions containing very little

or no O2. Listeria monocytogenes and Aeromonas hydrophila are facultative

anaerobes which are able to grow at both aerobic and anaerobic circumstances.

L. monocytogenes can cause mild flu-like symptoms in healthy people or the

more severe, life-threatening listeriosis in persons with deficient or immature

immune systems (FDA/USDA, 2003). Compared to other foodborne diseases,

listeriosis is relatively rare but it leads more often to severe consequences such

6 1.2 Modified atmosphere packaging (MAP) of fresh produce

as meningitis, abortion or stillbirth.

Although outbreaks of foodborne diseases are far more related to consump-

tion of meat, dairy products or seafoods as compared to fresh-cut vegetables

(Nguyen-the and Carlin, 1994), special attention must be paid to fresh-cut pro-

duce stored at MA conditions. MA packaging of refrigerated fresh-cut vegetables

might be cause for public health concern (Francis et al., 1999), for the following

reasons :

• Growth of natural competitors of pathogens, such as some spoilage aer-

obic micro-organisms, is suppressed at cool temperatures and gas atmo-

spheres applied in MA packaging. This may facilitate survival or growth

of pathogens.

• MA packaging can increase the shelf-life of the produce through better

maintenance of visual and sensory quality of the product. Pathogens may

have reached dangerous levels before visual microbial or other quality de-

cay occurs.

• Low oxygen MA packages subjected to temperature abuse, may become

anaerobic due to increased respiration. Under circumstances with no

or very low oxygen concentrations, anaerobic pathogens such as C. bo-

tulinum could grow and produce toxin. Larson et al. (1997) suggested

that the probability for toxin production before occurence of spoilage in

MA-packaged vegetables was less than 1 on 105. At refrigeration tem-

peratures however, spoilage was not great enough to render unacceptable

produce at the time of toxin production (Nguyen-the and Carlin, 1994).

1.2.3.3 Enzymatic brown discoloration

People buy with their eyes and learn from experience to associate desirable

qualities with a certain external appearance (Wills et al., 1998). One of the

most common causes of visual quality deterioration of fresh-cut produce is brown

discoloration. For fresh-cut lettuce, the colour of the cut surfaces is the most

important quality parameter.

Brown discoloration is the result of the oxidative reaction of the enzyme

polyphenol oxidase (PPO) with its phenolic substrates. In healthy plant tissue,

PPO is situated in the chloroplasts while the phenolic substrates are located

in the vacuole. When the cell structure is damaged, decompartmentalisation

occurs, allowing substrate and enzyme interaction and initiating the oxygen-

dependent enzymatic reaction.

General introduction 7

In low O2 MA packages, brown discoloration is reduced by reducing the oxy-

gen availability. Studies have demonstrated that in several cases high O2 levels

are inhibiting brown discoloration (Kader and Ben-Yehoshua, 2000) of fresh-cut

products. According to Day (1996), high O2 levels may cause substrate inhibi-

tion of PPO or high levels of produced quinones may cause feedback inhibition

of PPO.

1.3 Model-based design of MA packages

A model is a simplified (mathematical) representation of a real situation or of

real processes. In different areas of biological science, modelling can be used for

the mathematical description of various systems or processes. With regard to

the search for an optimal MA package for a specific application, modelling can

be a useful alternative for the trial and error approach that still predominates in

commercial practice (Peppelenbos and van ’t Leven, 1996; Hertog, 2003). First

of all, the design of a MA package requires a mathematical model relating the

product respiration rate to storage temperature and gas composition. Com-

bined with models describing gas transport through the package film, the gas

composition inside a package can be calculated. In steady state modelling, the

gas conditions at equilibrium conditions are determined. Dynamical modelling

using differential equations is required to determine the gas condition profile as

a function of time. Information on which equilibrium gas conditions are reached

and how fast, are the first parameters on which the development of a package

can be based.

More important however, is to know how product quality will change as a

function of time. For the food producer and food manufacturer the shelf-life

of the product is a major consideration (Phillips, 1996). According to Hertog

(2004), we need sub-models on how MAP is influencing the physiology of the

packaged product properties beyond their gas exchange in order to develop an

overall MAP model. Models that describe specific quality attributes of interest

(or more generally, keeping quality) as a function of the gas condition can be

considered for this purpose.

Additionally, models describing the growth of spoilage and/or pathogenic

micro-organisms can be included to provide information on microbial quality and

safety of the packaged produce. Predictive microbiology origins from the 1960s

and 1970s when kinetic models were used to address food spoilage problems

followed by the use of probability models to address food poisoning problems

(McMeekin et al., 2002). Predictive microbiology is based on the assumption

8 1.4 Objectives and outline of the thesis

that the responses of populations of micro-organisms to environmental factors

are reproducible and that, with information on past observations, it is possible

to predict microbial responses in a known environment (Ross, 1996). During the

first 30 to 40 years, predictive microbiology was mainly an academic activity.

At present times, its value is recognised as the scientific basis required for the

identification of hazards and critical control points and for the specification of

limits and corrective actions (McMeekin et al., 2002).

1.4 Objectives and outline of the thesis

The main objective of this thesis is to develop a model-based methodology for

the design of high O2 MA packages for strawberry and fresh-cut butterhead

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

as follows:

• to assess the effect of temperature and constant atmospheres with super-

atmospheric O2 levels alone or in combination with elevated CO2 levels

on the respiration of strawberry and fresh-cut butterhead lettuce and to

describe these effects using mathematical models

• to analyse and model the growth of Pseudomonas fluorescens, an impor-

tant spoilage micro-organism and Listeria innocua, a model organism for

the pathogenic bacterium Listeria monocytogenes, as affected by gas atmo-

spheres with superatmospheric O2 levels and elevated CO2 concentrations

• to investigate and model the effect of superatmospheric O2 concentrations

on brown discoloration of fresh-cut butterhead lettuce and on in vitro

activity of polyphenol oxidase (PPO), an important enzyme in brown dis-

coloration

• to incorporate the mathematical models in a MAP simulation model with

as the final goal the optimisation of high O2 MA packages and/or high O2

CA conditions for fresh-cut butterhead lettuce and strawberry

In Figure 1.1 a schematic representation of the structure of this thesis is

given.

In Chapter 2 the effect of low and high O2 levels combined with different

CO2 levels on the respiration rate of ‘Zendria’ fresh-cut butterhead lettuce and

‘Elsanta’ strawberry within the temperature range close to commercial practice

will be investigated. To describe the respiration rates over the temperature,

oxygen (low as well as high levels) and carbon dioxide range tested, a model

General introduction 9

based on Michaelis-Menten kinetics will be built. Michaelis-Menten equations

are often applied to describe respiration of fresh vegetables and fruits at O2 con-

centrations up to 21 kPa. Novel adaptations to the Michaelis-Menten equation

were made, to be able to describe the respiration at O2 concentrations above 21

kPa. The content of Chapter 2 has been published in Geysen et al. (2005b,c);

Escalona et al. (2006); Geysen et al. (2006b).

In Chapter 3 the effect of high O2 and elevated CO2 levels on the growth

of Pseudomonas fluorescens and Listeria innocua will be studied quantitatively

for the first time, in order to analyse the effect of both gases and to develop

predictive growth models. Pseudomonas fluorescens will be used as a model

organism for bacterial spoilage and Listeria innocua will be used as model or-

ganism for the pathogen Listeria monocytogenes. The growth models will be

based on the growth on a solid agar surface and will be validated on fresh-cut

butterhead lettuce. Growth will be studied at a temperature of 7 ◦C, being the

maximum allowed temperature for storage of refrigerated products in Belgium

(Anonymous, 1982). The results presented in Chapter 3 are published in Geysen

et al. (2005a,d, 2006a).

In Chapter 4 the impact of superatmospheric O2 levels on enzymatic brown

discoloration is investigated. The objective of Chapter 4 is twofold: the first

purpose is to describe the in vitro kinetics of PPO with respect to oxygen

concentrations from 5 to 100%, and to develop a novel model for the reaction

kinetics using chlorogenic acid (CGA) as a substrate. In the second part of

this chapter, brown discoloration of fresh-cut butterhead lettuce stored at high

oxygen and CO2 atmospheres will be evaluated and modelled. The results

presented in this chapter are partly published in Gomez et al. (2006).

In order to predict gas- and quality changes inside high O2 MA packages

of strawberry and fresh-cut butterhead lettuce, a set of dynamic models will

be incorporated in a MAP simulation model, which is the subject of Chap-

ter 5. Model equations that describe respiration, microbial growth and brown

discoloration previously developed in respectively Chapter 2, 3 and 4 will be

incorporated. The development (Section 5.2) and applications (Section 5.3) of

the MAP simulation model will be discussed.

Finally, general conclusions and suggestions for future work are formulated

in Chapter 6.

10 1.4 Objectives and outline of the thesis

Chapter 1: General introduction

Chapter 2: Analysis and modelling of

high oxygen effects onrespiration of strawberry and

fresh-cut butterhead lettuce

Chapter 4: Analysis and modelling of

high oxygen effects on in vitroPPO activity and browndiscoloration of fresh-cut

butterhead lettuce

Chapter 5: MAP simulation model incorporating models of

Chapters 2, 3 and 4

Chapter 6: General conclusions

Chapter 3: Analysis and modelling of

high oxygen effects on growthof Pseudomonas fluorescens

and Listeria innocua

Figure 1.1: Schematic representation of the thesis structure.

Chapter 2

Analysis and modelling of

high oxygen effects on

respiration of strawberry

and fresh-cut butterhead

lettuce

2.1 Introduction

Knowledge and modelling of respiration rates at different gas atmospheres is

required for the design of MA packages. Respiration measurements of fresh-cut

butterhead lettuce and strawberry are scarce especially under superatmospheric

oxygen concentrations. Furthermore, no models are available that include res-

piration under superatmospheric oxygen atmospheres until now.

Kader and Ben-Yehoshua (2000) reported that exposure to superatmospheric

O2 concentrations may stimulate, have no effect, or reduce rates of respiration

and ethylene production, depending on the commodity, maturity and ripeness

stage, O2 concentration, storage time and temperature, and concentrations of

CO2 and ethylene (C2H4) present in the atmosphere. The respiration of the

climacteric fruits apple (Kidd and West, 1934), avocado (Biale, 1946) and plum

(Claypool and Allen, 1951) was increased by the application of high oxygen con-

centrations through the acceleration of the onset of the climacteric rise, while

11

12 2.1 Introduction

the respiration of apricot (Claypool and Allen, 1948) was not influenced. Also

for non-climacteric fruits, the effect of high oxygen concentrations on the res-

piration is not unambiguous: respiration of the citrus fruits lemon (Biale and

Young, 1947) and grapefruit (Kader and Ben-Yehoshua, 2000) was stimulated,

while the respiration of cherries was unaffected (Claypool and Allen, 1948). The

respiration of ‘Camarosa’ strawberries at 5 ◦C was measured under superatmo-

spheric oxygen concentrations by Wszelaki and Mitcham (2000). After 1 day,

the CO2 production rate at 20 kPa O2 (139 nmol CO2 kg−1 s−1) was signif-

icantly higher than at 60, 80, 90 and 100 kPa O2 (95 nmol CO2 kg−1 s−1).

However, after 7 days of treatment, the CO2 production rate of fruit stored at

60 to 100 kPa O2 increased to values between 170 and 221 nmol CO2 kg−1 s−1,

whereas it only increased to 158 nmol CO2 kg−1 s−1 for fruit stored at 20 or 40

kPa O2.

The effect on vegetable respiration is also dependent on the type of veg-

etable. Respiration of artichoke kept in superatmospheric O2 did not change,

while respiration of carrots increased (Choudhury, 1939). Bulky storage organs

(e.g. potato tubers) showed an increased respiration under superatmospheric

oxygen concentrations. In leafy organs however, the influence of high oxygen

concentrations is not obvious. According to Jacxsens et al. (2001) the respiration

of shredded chicory endive at 4 ◦C increased with 45% and 57% at respectively

80% and 95% O2 compared to 3% O2. Barry-Ryan and O’ Beirne (1998) re-

ported that the respiration of shredded iceberg lettuce stored at 80% O2 + 10%

CO2 was comparable to the respiration at air + 10% CO2 but no further details

were given. The reduction in respiration as compared to air conditions was,

therefore, completely attributed to the carbon dioxide concentration and not to

the high oxygen concentration.

The diversity in respiration response to high oxygen concentrations makes it

difficult to formulate one single hypothesis on the mode of action of high oxygen

concentrations on metabolism which is valid for all products. For bulky storage

organs as e.g. potato tubers as well as for some other products that exhibit a

respiration increase due to high oxygen application, the effect has been explained

as the enhancement of cyanide-resistant respiration, which is responsible for

oxygen consumption in the presence of the metabolic inhibitor cyanide. The

cyanide-resistant respiration has a low affinity for O2 (Theologis and Laties,

1982). The physiological role of this mechanism would be the reduction of the

reactive oxygen species production. The alternative path provides a means for

oxidation of substrates without excessive production of reactive O2 species. In

this way, antioxidants are present in sufficient concentration to prevent damage

Analysis and modelling of high oxygen effects on respiration 13

to the phospholipids and proteins (Purvis, 1997). No hypotheses have been

suggested in literature for products with unaltered or decreased respiration rates

under high oxygen atmospheres.

When a vegetable or fruit product is packed in a modified atmosphere pack-

age, the gas atmosphere inside the package will be altered as a consequence of

the respiration of the product. Inversely, the respiration rate of the product

also depends on the gas composition inside the package, which will be chang-

ing until a steady state is reached. Prediction of gas compositions in modified

atmosphere packages thus requires prediction of respiration behaviour under

dynamic gas conditions. To meet this objective, dynamic models can be con-

structed. Dependence of the rate of respiration on O2 concentrations up to 21

kPa has been widely described by a Michaelis-Menten equation, which is the

simplest enzymatic kinetic mechanism (Peppelenbos and van ’t Leven, 1996).

The model is a simplification of the effect of the oxygen concentration on the

respiration, based on one limiting enzymatic reaction in which the substrate is

oxygen. According to Kader (1986) the decrease in respiration rate in response

to reduced O2 levels is not the result of suppression of the basal metabolism

mediated by cytochrome oxidase, but it appears to be a consequence of the

decrease of the activity of other oxidases with a lower affinity for oxygen. Also,

the measured affinity for oxygen in a fruit or vegetable is influenced by diffusion

properties as stated by Lammertyn et al. (2001). Without knowing the specific

background of the influence of oxygen on the metabolism, the Michaelis-Menten

equation tends to fit experimental respiration data of fruits and vegetables well

(Fonseca et al., 2002). According to Peppelenbos and van ’t Leven (1996) the

reducing effect of CO2 on respiration can be modelled by four types of inhibi-

tion in the Michaelis-Menten equation: the competitive type, the uncompetitive

type, the linear mixed type being a combination of both previous types and the

noncompetitive type. Until now, no models to describe respiration up to 100

kPa O2 have been suggested in literature.

The objective of this chapter is to determine the effect of low and high

O2 levels combined with different CO2 levels on the respiration rate of fresh-

cut butterhead lettuce and strawberry within the temperature range close to

commercial practice. A model based on the Michaelis-Menten equation will be

built to describe the respiration rate over the temperature, oxygen (low as well

as high levels) and carbon dioxide range tested.

This chapter consists of four sections. In Section 2.2 the used experimental

techniques and methods as well as the model structure will be outlined. The

results and the model application with special emphasis on the description of the

14 2.2 Materials and Methods

high oxygen effect will be presented in Section 2.3 and discussed and compared

to literature results in Section 2.4. Finally, conclusions will be formulated in

Section 2.5.

The content of this chapter has been published in Geysen et al. (2005b,c);

Escalona et al. (2006); Geysen et al. (2006b).

2.2 Materials and Methods

2.2.1 Experimental setup

2.2.1.1 Product material

Samples of 200 g fresh-cut butterhead lettuce (Lactuca sativa L.) or 40 strawber-

ries (Fragaria x ananassa Duchesne) were put into glass jars of 1.7 L. Butterhead

lettuces (cultivar ‘Zendria’) grown in a commercial greenhouse in Mechelen (Bel-

gium) were used. Strawberries of cultivar ‘Elsanta’ were purchased from Veiling

Borgloon, a Belgian fruit auction. Lettuces and strawberries were transported

to the laboratory and stored at 1 ◦C until used the next morning. Intact straw-

berry fruits were placed in the jars after being randomised. The lettuce heads

were processed in a clean room at 7 ◦C, after soiled and decayed external leaves

were eliminated. First, the central stem was removed and the lettuce was hand

cut in pieces of about 2 cm using a sharp knife. The cut lettuce immediately

was immersed for 1 min in tap water at 5 ◦C and centrifuged using a domestic

centrifuge, which removed excess water without causing any visible damage to

the lettuce.

2.2.1.2 Gas and temperature conditions

Two or three jars filled with product were connected in series in incubation

chambers at 1, 5 and 9 ◦C (for fresh-cut butterhead lettuce) or at 2, 7 and 14 ◦C

(for strawberry) and flushed with the humidified gas mixtures. Pure gases

(oxygen, carbon dioxide and nitrogen) were mixed using mass-flow controllers

(model 5850S, Brooks Instrument, The Netherlands) to obtain the appropriate

gas mixtures.

The following gas atmospheres were applied to the fresh-cut butterhead let-

tuce (kPa O2 : kPa CO2 : kPa N2) : (0 : 0 : 100) ; (2 : 0 : 98) ; (5 : 0 :

95) ; (10 : 0 : 90) ; (20 : 0 : 80) ; (50 : 0 : 50) ; (75 : 0 : 25) ; (100 : 0 :

0) ; (0 : 10 : 90) ; (2 : 10 : 88) ; (5 : 10 : 85) ; (10 : 10 : 80) ; (20 : 10 :

70) ; (50 : 10 : 40) ; (75 : 10 : 15) ; (0 : 20 : 80) ; (2 : 20 : 78) ; (5 : 20 :

75) ; (10 : 20 : 70) ; (20 : 20 : 60) ; (50 : 20 : 30) ; (75 : 20 : 5). The gas

Analysis and modelling of high oxygen effects on respiration 15

O2 between 0 and 100%

CO2 between 0 and 20%

O2

CO2 N2

(20, 20, 60)(20, 10, 70)

(20, 0, 80)

(100, 0, 0)

(75, 0, 25)(75, 20, 5)

(50, 0, 50)

(10, 0, 90)(5, 0, 95)(2, 0, 98)(0, 0, 100)

(75, 10, 15)

(50, 10, 40)(50, 20, 30)

(10, 20, 70)(5, 20, 75)

(0, 20, 80)(2, 20, 78)

Figure 2.1: Tested gas conditions (marked with crosses) for respiration measurements

of fresh-cut butterhead lettuce. The region of interest is marked in grey and is limited

in carbon dioxide between 0% and 20%. The corresponding gas concentrations are

given between brackets in the following order: (% O2, % CO2, % N2).

conditions are represented in Figure 2.1. More gas combinations were tested at

low O2 concentrations since information at low O2 levels is needed to estimate

Km-values of the Michaelis-Menten models (see below, Section 2.2.3). Three

jars per treatment were measured. Because of the high number of treatments,

the experiment was split up in blocks of 4 to 5 gas conditions that were carried

out at the same time. As a consequence, different batches of lettuce were used.

As a control, the gas mixture with 20 kPa O2 + 0 kPa CO2 was applied in each

separate experiment in order to track the influence of the batch of lettuce.

For strawberry two separate experiments were carried out. In the first ex-

periment, strawberries were stored at 3 different temperatures (2, 7 and 14 ◦C),

combined with the following six gas mixtures (kPa O2 : kPa CO2 : kPa N2) :

(20 : 0 : 80) ; (60 : 0 : 40) ; (100 : 0 : 0) ; (20 : 20 : 60) ; (50 : 20 : 30) ; (80

: 20 : 0). Per treatment two jars were measured. In the second experiment, 2

temperatures (2 and 7 ◦C) were combined with the following eight gas mixtures

(kPa O2 : kPa CO2 : kPa N2) : (0 : 0 : 100) ; (2 : 0 : 98) ; (5 : 0 : 95) ; (20

: 0 : 80) ; (60 : 0 : 40) ; (0 : 20 : 80) ; (20 : 20 : 60) ; (50 : 20 : 30). Here,

three jars per treatment were measured. The gas conditions of both strawberry

experiments are represented in Figure 2.2.

16 2.2 Materials and Methods

O2 between 0 and 100%

CO2 between 0 and 20%

O2

CO2 N2

(20, 20, 60) (20, 0, 80)

(100, 0, 0)

(80, 20, 0)

(60, 0, 40)

(5, 0, 95)(2, 0, 98)(0, 0, 100)

(50, 20, 30)

(0, 20, 80)

Figure 2.2: Tested gas conditions of experiment 1 (marked with crosses) and experi-

ment 2 (marked with circles) for respiration measurements of strawberry. The region

of interest is marked in grey and is limited in carbon dioxide between 0% and 20%. The

corresponding gas concentrations are given between brackets in the following order:

(% O2, % CO2, % N2).

2.2.1.3 Respiration measurements

The glass jars were flushed overnight to give the products the time to reach

an equilibrium with the applied gas atmosphere. The headspace composition

(O2, CO2 and N2) was measured with a micro gas chromatograph (micro-GC,

CP2003-P, equipped with a Molsieve 5A molecular sieve column for analysis of

O2 and N2 and a PoraplotQ column for analysis of CO2; Varian-Chrompack,

Bergen op Zoom, The Netherlands) immediately after closing the jars and again

after 2 to 4 hours. The concentration changes were used to calculate the O2

consumption and CO2 production rates. The jars were flushed again overnight

and the measurements were repeated the next days. The O2 consumption and

CO2 production rates were calculated as follows:

rO2 = − 1M

∆nO2

∆t(2.1)

rCO2 =1M

∆nCO2

∆t(2.2)

rO2 and rCO2 are the oxygen consumption and carbon dioxide production

rate (mol kg−1 s−1), M is the mass of the product (kg), ∆t is the time interval

(s) between the first and the second measurement, ∆nO2 and ∆nCO2 are the

Analysis and modelling of high oxygen effects on respiration 17

changes in moles of oxygen and carbon dioxide and are calculated using the

universal gas law as shown in equation (2.3).

∆n(C)O2 =∆p(C)O2Vfree

RT(2.3)

Where R is the universal gas constant (= 8.314 J mol−1 K−1), T is the

temperature (K) and Vfree is the free volume in the jar and calculated as in

equation (2.4).

Vfree = Vjar −M

ρ(2.4)

where Vjar is the volume of the jar (in m3) and ρ is the density of the product

(in kg m−3) that was determined using Archimedes’ law. ∆p(C)O2 is the change

in oxygen or carbon dioxide partial pressure (Pa) in the jar and was calculated

according to equation (2.5).

∆p(C)O2 =(

x(C)O2

100

(pstart + pend

2

))2

−(

x(C)O2

100

(pstart + pend

2

))1

(2.5)

where pstart and pend are the pressures (in mbar) in the jar respectively at

the beginning and the end of sample taking monitored with a pressure sensor

(PTX 520-0, Druck, The Netherlands); subscript 1 denotes the determination

of the gas composition directly after closing the jar while subscript 2 refers to

the second measurement of the headspace gas composition after the product has

respired in the closed jar for a certain period ∆t; and x(C)O2 is the measured

percentage of O2 or CO2 in the jar.

2.2.2 Analysis of variance

First, the results were examined for effects of variables other than temperature

and gas composition. It was checked whether the position of the jars (first,

second or third in a serial connection to the gas inlet) and the storage time had

an influence on the measured respiration rate. The batch effect was examined

based on the results of the gas mixture with 20 kPa O2 + 0 kPa CO2 which

was applied in all blocks. To analyse the different effects, ANOVA was carried

out using the GLM procedure of the software package SAS/STAT® version 8.2

(SAS Institute Inc., Cary, North Carolina, United States).

18 2.2 Materials and Methods

2.2.3 Modelling the respiration rates

2.2.3.1 Michaelis-Menten models per temperature-CO2 combination

We used two steps to obtain models to describe the respiration rate of straw-

berry and fresh-cut butterhead lettuce as a function of temperature, oxygen

and carbon dioxide concentration. In the first step, the influence of the oxy-

gen concentration on the oxygen consumption and carbon dioxide production

rate was described with a Michaelis-Menten equation for each temperature-CO2

combination. The O2 consumption rate is given by equation (2.6).

rO2 = VmO2

pO2

KmO2 + pO2

(2.6)

where rO2 is the O2 consumption rate (mol kg−1 s−1), KmO2 is the Michaelis-

Menten constant for O2 (kPa), VmO2 is the maximum oxygen consumption rate

(mol kg−1 s−1) and pO2 the oxygen partial pressure (kPa). The CO2 production

rate is given by equation (2.7). In this first stage, no term for fermentative

metabolism at low oxygen concentrations was included and the CO2 production

rate was considered proportional to rO2 at all oxygen concentrations.

rCO2 = rqoxrO2 (2.7)

where rCO2 is the CO2 production rate (in mol kg−1 s−1) and rqox is the respi-

ratory quotient.

Equation (2.6) and (2.7) were fitted simultaneously to the data of each tem-

perature - CO2 combinations. This was done for all nine temperature - CO2

combinations tested for fresh-cut butterhead lettuce (nine combinations of 1, 5,

9 ◦C and 0, 10, 20 kPa CO2) and thus resulted in nine sets of estimated KmO2 ,

VmO2 and rqox values. Consequently, 27 parameters (three parameters times

nine curves) were used in this approach. For strawberry, this approach resulted

in six sets of the three model parameters, which resulted in 18 parameters in

total.

2.2.3.2 Overall Michaelis-Menten models

In a second step an overall model was defined to describe the respiration rates

of all experimental conditions together as a function of not only oxygen (as

does the basic Michaelis-Menten equation defined in equation (2.6) and (2.7))

but also of temperature and carbon dioxide. The model also had to be able to

account for possible effects of superatmospheric oxygen concentrations. Differ-

ent overall models were tried and compared based on the RMSE (root mean

Analysis and modelling of high oxygen effects on respiration 19

squared error). To model the inhibitive effect of carbon dioxide on the respi-

ration rate, four types of inhibition can be considered: competitive inhibition

(equation (2.8)), uncompetitive inhibition (equation (2.9)), linear mixed inhi-

bition (equation (2.10)) and noncompetitive inhibition (equation (2.11)) each

affecting the apparent KmO2 and VmO2 in a specific fashion as shown in Table

2.1 (Fersht, 1977).

rO2 = VmO2

pO2

KmO2 (1 + pCO2/KmcCO2) + pO2

rO2,i (2.8)

KmcCO2 is the Michaelis-Menten constant for competitive inhibition of CO2.

rO2 = VmO2

pO2

KmO2 + pO2 (1 + pCO2/KmuCO2)rO2,i

(2.9)

KmuCO2 is the Michaelis-Menten constant for uncompetitive inhibition of

CO2.

rO2 = VmO2

pO2

KmO2 (1 + pCO2/KmcCO2) + pO2 (1 + pCO2/KmuCO2)rO2,i

(2.10)

rO2 = VmO2

pO2

(KmO2 + pO2) (1 + pCO2/KmnCO2)rO2,i (2.11)

KmnCO2 is the Michaelis-Menten constant for noncompetitive inhibition of

CO2. In all four previous models, rO2,i is the term for noncompetitive inhibition

from superatmospheric oxygen concentrations and is defined by equation (2.12).

Unlike the terms to describe carbon dioxide inhibition (equations (2.8) to (2.11))

and temperature effects (equation (2.15)) which are generally applied terms, this

term was never described before. Its use will be motivated in section 2.3.2.2. In

models in which inhibition from superatmospheric oxygen concentrations was

not considered this term was set to 1.

rO2,i =1

1 + pO2/KmO2i(2.12)

KmO2i (kPa) is a parameter describing noncompetitive oxidative inhibition

of superatmospheric oxygen concentrations. The CO2 production rate is given

by equation (2.13).

rCO2 = rqoxrO2 + rCO2f (2.13)

rCO2 is the CO2 production rate, rqox is the respiratory quotient at non-

fermentative oxygen concentrations. The term rCO2f is defined by equation

(2.14) and was included in all overall models.

20 2.3 Results

Table 2.1: Overview of the effects of different types of inhibitors on apparent VmO2

and KmO2 values.

Competitive Uncompetitive Linear mixed Noncompetitive

VmO2 No effect Decrease Decrease Decrease

KmO2 Increase Decrease Increase No effect

rCO2f = VmCO2

11 + pO2/KmO2f

(2.14)

VmCO2 is the maximum respiration rate for CO2, KmO2f is a parameter

describing the inhibition on the fermentative metabolism by O2. The Arrhenius

equation was used to describe the temperature dependence of VmO2 and VmCO2

(equation (2.15)).

Vm(C)O2 = Vm(C)O2ref exp(−EaVm(C)O2

R

(1T− 1

Tref

))(2.15)

Where Vm(C)O2 is the maximum respiration rate of oxygen or carbon diox-

ide, Vm(C)O2ref is the reference maximum respiration rate of oxygen or carbon

dioxide, Tref is the reference temperature (278 K (5 ◦C) for fresh-cut butterhead

lettuce and 283 K (10 ◦C) for strawberry), T is the temperature (K), EaVm(C)O2

is the activation energy, Rgas is the ideal gas constant.

The equations for the oxygen consumption rate (equation (2.8), (2.9), (2.10)

or (2.11) depending on the considered type of CO2 inhibition) were solved si-

multaneously with equation (2.13) (the expression for the carbon dioxide pro-

duction rate) on all data (all experimental conditions taken together). For non-

linear regression analyses, the NLIN procedure of SAS/STAT® was used. The

Levenberg-Marquardt optimisation method was used as nonlinear least-square

search method. The solutions for the parameters were restricted to positive

values. Parameters were considered significant when their 95% approximate

confidence interval did not include zero.

2.3 Results

2.3.1 Analysis of variance

The position of the jars (first, second or third behind the gas inlet) had no influ-

ence on the measured respiration rate, so that the measurements of the two or

three different jars per treatment can be seen as repetitions. For model develop-

ment purposes, only data from the two (for lettuce) or three (for strawberry) first

Analysis and modelling of high oxygen effects on respiration 21

days of storage where considered since respiration rates were constant during

these periods. Respiration rates of lettuce stored longer than 2 days decreased.

There was a significant batch effect on the respiration rate. Deviant batches

were excluded for further analyses.

2.3.2 Modelling the respiration rates

2.3.2.1 Michaelis-Menten models per temperature-CO2 combination

In a first stage, the effect of the O2 concentration on the O2 consumption and

CO2 production rate was described by a Michaelis-Menten equation (see equa-

tion (2.6) and (2.7)) for each temperature-CO2 combination separately. The O2

consumption rate and the CO2 production rate were fitted simultaneously. The

predicted together with the measured oxygen consumption and carbon dioxide

production rates per temperature-CO2 combination are shown in Figure 2.3 for

fresh-cut butterhead lettuce and Figure 2.4 for strawberry.

For fresh-cut butterhead lettuce and strawberry respectively nine and six sets

of the parameters KmO2 , VmO2 and rqox and their approximate 95% confidence

intervals were estimated. The parameter estimates are represented graphically

as a function of temperature and carbon dioxide concentration in Figure 2.5 for

lettuce and Figure 2.6 for strawberry.

For both fresh-cut butterhead lettuce and strawberry, the maximum respi-

ration rate (VmO2) was significantly higher at higher temperatures. Depending

on the applied CO2 level, the VmO2 of fresh-cut butterhead lettuce varied from

40 to 55 nmol kg−1 s−1 at 1 ◦C, from 67 to 86 nmol kg−1 s−1 at 5 ◦C and from

115 to 147 nmol kg−1 s−1 at 9 ◦C. The respiration rates of strawberry were

higher than those of fresh-cut butterhead lettuce and VmO2 of strawberry at

20 and 0 kPa CO2 respectively equalled 78 and 85 nmol kg−1 s−1 at 2 ◦C, 133

and 193 nmol kg−1 s−1 at 7 ◦C and 234 and 360 nmol kg−1 s−1 at 14 ◦C. The

carbon dioxide concentration had a significant influence on the VmO2-values of

both products, although in both cases to a smaller extent than temperature.

In the case of fresh-cut butterhead lettuce, the carbon dioxide effect on VmO2

was ambiguous. At all temperatures, VmO2 at 10 kPa CO2 was significantly

smaller than at 0 kPa CO2. By application of 10 kPa CO2, the O2 consumption

rate was reduced to approximately 80% of that at 0 kPa CO2. At 20 kPa CO2

however, VmO2 was the same as at 0 kPa CO2 at 1 and 9 ◦C. For strawberry,

VmO2 at 20 kPa CO2 was significantly smaller than at 0 kPa CO2 at 7 ◦C and

14 ◦C. At the low temperature of 2 ◦C, the CO2 concentration did not influence

the maximum respiration rate of strawberry.

22 2.3 Results

0 50 1000

50

100

150

200

O2 c

onsu

mpt

ion

rate

(nm

ol.k

g−1 .s

−1 )

1°C

0 50 1000

50

100

150

200

O2 concentration (kPa)

5°C

0 50 1000

50

100

150

200

9°C

0 50 1000

50

100

150

200

CO

2 pro

duct

ion

rate

(nm

ol.k

g−1 .s

−1 ) 1°C

0 50 1000

50

100

150

200

O2 concentration (kPa)

5°C

0 50 1000

50

100

150

200

9°C

Figure 2.3: Measured (symbols) and modelled (lines) oxygen consumption and car-

bon dioxide production rates (nmol kg−1 s−1) of fresh-cut butterhead lettuce as a

function of oxygen concentration (kPa) at different temperatures and carbon dioxide

concentrations (0 kPa CO2: © and full lines; 10 kPa CO2: × and dashed lines; 20

kPa CO2: 4 and dotted lines). The Michaelis-Menten equation was applied to every

CO2-temperature combination.

Analysis and modelling of high oxygen effects on respiration 23

0 50 1000

100

200

300

400

O2 c

onsu

mpt

ion

rate

(nm

ol.k

g−1 .s

−1 )

2°C

0 50 1000

100

200

300

400

O2 concentration (kPa)

7°C

0 50 1000

100

200

300

40014°C

0 50 1000

100

200

300

400

CO

2 pro

duct

ion

rate

(nm

ol.k

g−1 .s

−1 ) 2°C

0 50 1000

100

200

300

400

O2 concentration (kPa)

7°C

0 50 1000

100

200

300

40014°C

Figure 2.4: Measured (symbols) and modelled (lines) oxygen consumption and car-

bon dioxide production rates (nmol kg−1 s−1) of strawberry as a function of oxygen

concentration (kPa) at different temperatures and carbon dioxide concentrations (0

kPa CO2: © and full lines; 20 kPa CO2: 4 and dotted lines). The Michaelis-Menten

equation was applied to every CO2-temperature combination.

0 10 200

50

100

150

Vm

,O2 (

nmol

.kg−

1 .s−

1 )

0 10 20

0

0.5

1

Km

O2 (

kPa)

CO2 concentration (kPa)0 10 20

0

0.5

1

1.5

2

r qox

Figure 2.5: Estimated KmO2 , VmO2 and rqox values and approximate 95% confidence

intervals of fresh-cut butterhead lettuce as a function of temperature (1 ◦C: black; 5 ◦C:

grey; 9 ◦C: white) and carbon dioxide concentration.

24 2.3 Results

0 200

100

200

300

400

Vm

,O2 (

nmol

.kg−

1 .s−

1 )

0 20

0

2

4

6

Km

O2 (

kPa)

CO2 concentration (kPa)0 20

0

0.5

1

1.5

2

r qox

Figure 2.6: Estimated KmO2 , VmO2 and rqox values and approximate 95% confidence

intervals of strawberry as a function of temperature (2 ◦C: black; 7 ◦C: grey; 14 ◦C:

white) and carbon dioxide concentration.

The approximate 95% confidence intervals on the estimates of KmO2 of fresh-

cut lettuce stored at 0 or 20 kPa CO2 were very large and there was no significant

difference between KmO2 at 0 or 20 kPa CO2. At 10 kPa however, the confidence

intervals of the KmO2 values were smaller and KmO2 values were significantly

smaller than at 0 and 20 kPa CO2. The average KmO2 value was 0.4 kPa at

gas conditions with 0 and 20 kPa CO2 and 0.03 kPa at gas conditions with 10

kPa CO2. The lower error in the KmO2 estimates at 10 kPa CO2 is related

to the larger amount of measurements at low oxygen concentrations (< 5 kPa)

at this CO2 concentration. At all CO2 concentrations, KmO2 was temperature

independent. Adequate estimation of KmO2 values for strawberry was hampered

due to a lack of data points at low oxygen concentrations at several temperature-

CO2 conditions. For this reason, no further conclusions will be drawn on this

parameter at this stage.

The respiratory quotient (rqox) of strawberry showed to be independent of

temperature and CO2 concentrations. The average rqox was 0.69. rqox values of

fresh-cut lettuce were not dependent on temperature, but there was an influence

of carbon dioxide concentration. At 10 and 20 kPa CO2, the average rqox was

equal to 1.1 and 0.9 respectively, whereas at 0 kPa CO2 the average rqox was

lower and equalled 0.8.

2.3.2.2 Modelling high oxygen effects

The Michaelis-Menten equation, as it is applied in the previous section, is able to

describe respiration rates increasing with increasing O2 concentrations (defined

by the KmO2 value) and evolving to a maximum respiration rate VmO2 . As

mentioned before, many authors successfully described respiration rates of fruits

and vegetables up to 21 kPa O2 using this equation. Looking at the model

Analysis and modelling of high oxygen effects on respiration 25

fits in the previous section, the equation also gave a good representation of

strawberry respiration data up to 100 kPa O2. However, the Michaelis-Menten

equation was not capable to describe the decreased respiration rates of fresh-cut

butterhead lettuce at high oxygen concentrations. The term rO2,i, which is given

in equation (2.12) was introduced to account for this high oxygen inhibition

effect. Unlike the terms to describe carbon dioxide inhibition (equations (2.8)

to (2.11)) and temperature effects (equation (2.15)) which are generally applied

terms, this term was never described before. This section is therefore dedicated

to the development, characteristics and application of this term, based on the

acquired respiration data of fresh-cut butterhead lettuce. The high oxygen

inhibition effect was considered as a noncompetitive inhibition (see equation

(2.12)). Noncompetitive inhibition causes a decrease in apparent VmO2 and

does not affect apparent KmO2 values (Fersht, 1977), which was also suggested

by the experimental results (see Figure 2.3). Including the noncompetitive term

in equation (2.6) yields

rO2 = VmO2

pO2

KmO2 + pO2

· 11 + pO2/KmO2i

(2.16)

To demonstrate the effect of the value of the extra parameter KmO2i, equa-

tion (2.16) was solved for KmO2i values equal to 10, 50, 200, 800 or +∞ kPa,

with VmO2 and KmO2 respectively 80 nmol kg−1 s−1 and 0.44 kPa. The results

of the simulation are shown in Figure 2.7. When KmO2i is +∞ rO2,iis equal to

1, equation (2.16) reduces to the original Michaelis-Menten equation. The lower

the value of KmO2i, the greater the effect of high oxygen concentrations on the

respiration rates. To demonstrate the use of equation (2.16), it was fitted to

the O2 consumption data of two combinations of temperature and CO2 where

the inhibitive effect of high oxygen concentrations was very clear, namely 5 ◦C

combined with 20 kPa CO2 and 9 ◦C combined with 0 kPa CO2. The model fits

are represented in Figure 2.8 and the parameter estimates are listed in Table

2.2. For both conditions, equation (2.16) fitted the experimental data better

than equation (2.6), as illustrated by the lower RMSE value.

2.3.2.3 Overall Michaelis-Menten models

Based on the results of the first stage modelling, overall models were constructed.

Temperature only influenced the estimates of VmO2 . The temperature depen-

dency of VmO2 is described by an Arrhenius equation in all overall models (equa-

tion (2.15)). Also fermentation (equation (2.14)) was considered in all overall

models.

26 2.3 Results

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100

O2 concentration (kPa)

O2 c

onsu

mpt

ion

rate

(nm

ol.k

g −

1 .s−

1 )

KmO2i=

Infinite

800

200

50

10

Figure 2.7: Simulated O2 consumption rates as a function of O2 concentration at

different values of KmO2i (10, 50, 200, 800 and +∞ kPa) with VmO2=80 nmol.kg−1.s−1

and KmO2=0.44 kPa).

0 10 20 30 40 50 60 70 80 90 100−50

0

50

100

150

200

O2 concentration (kPa)

O2 c

onsu

mpt

ion

rate

(nm

ol.k

g−1 .s

−1 )

Figure 2.8: Comparison of fitting equation (2.6) (Michaelis-Menten equation, dashed

lines) and equation (2.16) (Michaelis-Menten equation including a high oxygen inhibi-

tion term, solid lines) to the O2 consumption data (©) of fresh-cut butterhead lettuce

stored at 9 ◦C without CO2 (in black) and at 5 ◦C with 20 kPa CO2 (in grey).

Analysis and modelling of high oxygen effects on respiration 27

Table 2.2: Comparison of parameter estimates and RMSE applying equation (2.6)

(Michaelis-Menten equation) either equation (2.16) (Michaelis-Menten equation in-

cluding a high oxygen inhibitive term) to O2 consumption data of fresh-cut butterhead

lettuce of two experimental conditions.

Condition Parameters Estimates ± 95% C.L.

Equation (2.6) Equation (2.16)

9 ◦C, 0 kPa CO2 VmO2 (nmol kg−1 s−1) 154 ± 10 177 ± 21

KmO2 (kPa) 0.6 ± 0.5 1.0 ± 0.7

KmO2i (kPa) - 308 ± 265

RMSE 27 25

5 ◦C, 20 kPa CO2 VmO2 (nmol kg−1 s−1) 83 ± 7 109 ± 26

KmO2 (kPa) 1.0 ± 0.7 2.0 ± 1.6

KmO2i (kPa) - 134 ± 126

RMSE 14 12

As mentioned in section 2.3.2.1 the effect of elevated carbon dioxide levels

on fresh-cut butterhead lettuce was ambiguous. Where at CO2 levels of 10 kPa

the KmO2 and VmO2 values were significantly lower than at 0 kPa CO2, the

KmO2 and VmO2 values at 20 kPa CO2 were comparable to those at 0 kPa CO2.

Because the inhibitive effect of carbon dioxide is only valid up to 10 kPa CO2,

respiration data of lettuce stored at 20 kPa CO2 were not further considered in

the overall modelling. For strawberry, KmO2 and VmO2 values were significantly

lower at 20 kPa CO2 compared to at 0 kPa CO2. In overall model #1 and

#3 an uncompetitive inhibition term for CO2 was included. The choice for

uncompetitive inhibition was based on the observation that increasing carbon

dioxide concentrations (from 0 to 10 kPa) resulted in a decrease of the apparent

KmO2 and VmO2 value which is indicative for uncompetitive inhibition. In model

#2, the CO2 inhibition is alternatively described by a noncompetitive inhibition

term. This type of inhibition is characterised by a decrease in apparent VmO2

values, while apparent KmO2 values are not affected. This can also be the case

since some KmO2 values may be estimated less accurately because of a lack of

data points at low oxygen levels.

The inhibitive effect of superatmospheric oxygen concentrations was included

in overall models #1 and #2 by including a noncompetitive inhibition term. In

model #3 the high O2 inhibition term is omitted. An overview of the included

terms in the overall models is given in Table 2.3.

The overall models were fitted on all respiration data together. For fresh-

cut butterhead lettuce, respiration data at 20 kPa CO2 were omitted for overall

28 2.3 Results

Table 2.3: Overview of included terms in the overall models. Temperature depen-

dency of VmO2 was described according to Arrhenius in all overall models.

Overall

model

Uncompetitive

CO2 inhibition

Noncompetitive

CO2 inhibition

Noncompetitive

O2 inhibition

Fermen-

tation

Model #1 Yes No Yes Yes

Model #2 No Yes Yes Yes

Model #3 Yes No No Yes

Table 2.4: Estimated model parameter values with 95% confidence intervals, RMSE,

R2adj and number of parameters of the three overall models for fresh-cut butterhead

lettuce.

Model parameter Model #1 Model #2 Model #3

VmO2ref (nmol kg−1 s−1) 97 ± 5 96 ± 5 92 ± 3

EaVmO2 (kJ mol−1) 85 ± 5 85 ± 5 85 ± 5

VmCO2ref (nmol kg−1 s−1) 50 ± 6 50 ± 6 50 ± 6

EaVmCO2 (kJ mol−1) 24 ± 21 24 ± 21 24 ± 22

rqox (-) 0.78 ± 0.04 0.77 ± 0.04 0.78 ± 0.04

KmO2 (kPa) 1.1 ± 0.4 1.0 ± 0.3 0.9 ± 0.3

KmnCO2 or KmuCO2 (kPa) 39 ± 9 42 ± 10 39 ± 9

KmO2i (kPa) 984 ± 878 1008 ± 922 -

KmO2f (kPa) 2.1 ± 1.4 2.2 ± 1.5 2.3 ± 1.5

RMSE (nmol kg−1 s−1) 17.5 17.6 17.6

R2adj 0.82 0.82 0.82

Number of parameters 9 9 8

modelling. The parameter estimates with their 95% confidence limits are repre-

sented in Table 2.4 for fresh-cut butterhead lettuce and Table 2.5 for strawberry.

For fresh-cut butterhead lettuce overall model #1 fits the data well as based

on the R2adj (0.82). Nevertheless, overall model #2 where CO2 was included as

a noncompetitive inhibition term described the data equally well. Also, when

the high oxygen inhibition term was omitted (overall model #3), the model

still predicted the data equally well as based on the RMSE and the adjusted R2

(R2adj). The model predictions for model #2 are plotted in Figure 2.9. In overall

model #1, #2 and #3 all model parameters were significant. The estimated

Michaelis-Menten constant for O2 was relatively small (KmO2 = 1.1, 1.0 and 0.9

kPa respectively) and caused a steep curve at low oxygen concentrations. At

low O2 levels a small change in oxygen concentration resulted in a large change

in respiration rate. High oxygen concentrations also inhibited the respiration

Analysis and modelling of high oxygen effects on respiration 29

Table 2.5: Estimated model parameter values with 95% confidence intervals, RMSE,

R2adj and number of parameters of the three overall models for strawberry.

Model parameter Model #1 Model #2 Model #3

VmO2ref (nmol kg−1 s−1) 242 ± 5 242 ± 4 242 ± 5

EaVmO2 (kJ mol−1) 64 ± 2 64 ± 17 64 ± 2

VmCO2ref (nmol kg−1 s−1) 175 ± 36 175 ± 36 175 ± 36

EaVmCO2 (kJ mol−1) 65 ± 17 65 ± 17 65 ± 17

rqox (-) 0.66 ± 0.02 0.66 ± 0.02 0.66 ± 0.02

KmO2 (kPa) 1.2 ± 0.2 1.2 ± 0.2 1.2 ± 0.2

KmnCO2 or KmuCO2 (kPa) 51 ± 5 53 ± 5 51 ± 5

KmO2i (kPa) Not significant Not significant -

KmO2f (kPa) 0.14 ± 0.09 0.14 ± 0.09 0.14 ± 0.09

RMSE (nmol kg−1 s−1) 15 15 15

R2adj 0.96 0.96 0.96

Number of parameters 9 9 8

rate, with KmO2i equal to 984 (± 878) kPa in overall model #1 which results

in a rather limited inhibition, although statistically significant, due to superat-

mospheric O2 concentrations. However, as previously mentioned, excluding the

superatmospheric oxygen inhibition term from the overall model did not greatly

decrease the predictive power of the overall model. The Michaelis-Menten con-

stant for uncompetitive CO2-inhibition KmuCO2 was 39 ± 9 kPa in overall model

#1 and #3. The Michaelis-Menten constant for noncompetitive CO2-inhibition

KmnCO2 was 42 ± 10 kPa in overall model #2. The ratio between produced

CO2 and consumed O2 at high oxygen concentrations expressed as rqox was

estimated 0.78.

For strawberry, all model parameters in model #1 and #2 were significant

except for KmO2i. Based on the R2adj (0.96) and RMSE (15), the models with

uncompetitive and noncompetitive CO2 inhibition term were fitting the data

equally well. The model predictions for model #3 are plotted in Figure 2.10.

2.4 Discussion

Several authors previously reported on the influence of temperature, carbon

dioxide and oxygen concentrations up to 20 kPa on the oxygen consumption

and/or carbon dioxide production of different cultivars of strawberries (Renault

et al., 1994; Hertog et al., 1999) and fresh-cut lettuce (Smyth et al., 1998;

Gorny, 2001). Gorny (2001) showed that the carbon dioxide production rates

30 2.4 Discussion

0 50 1000

50

100

150

200

250

O2 c

onsu

mpt

ion

rate

(nm

ol.k

g−1 .s

−1 )

1°C

0 50 1000

50

100

150

200

250

O2 concentration (kPa)

5°C

0 50 1000

50

100

150

200

250

9°C

0 50 1000

50

100

150

200

250

CO

2 pro

duct

ion

rate

(nm

ol.k

g−1 .s

−1 )

1°C

0 50 1000

50

100

150

200

250

O2 concentration (kPa)

5°C

0 50 1000

50

100

150

200

250

9°C

Figure 2.9: Measured (symbols) and modelled (lines) oxygen consumption and car-

bon dioxide production rates (nmol kg−1 s−1) of fresh-cut butterhead lettuce as a

function of oxygen concentration (kPa) at different temperatures and carbon dioxide

concentrations (0 kPa CO2: © and full lines; 10 kPa CO2: × and dashed lines).

Overall model #2 was applied to all respiration data together.

Analysis and modelling of high oxygen effects on respiration 31

0 50 1000

100

200

300

400

O2 c

onsu

mpt

ion

rate

(nm

ol.k

g−1 .s

−1 )

2°C

0 50 1000

100

200

300

400

O2 concentration (kPa)

7°C

0 50 1000

100

200

300

400

14°C

0 50 1000

100

200

300

400

CO

2 pro

duct

ion

rate

(nm

ol.k

g−1 .s

−1 )

2°C

0 50 1000

100

200

300

400

O2 concentration (kPa)

7°C

0 50 1000

100

200

300

40014°C

Figure 2.10: Measured (symbols) and modelled (lines) oxygen consumption and

carbon dioxide production rates (nmol kg−1 s−1) of strawberry as a function of oxygen

concentration (kPa) at different temperatures and carbon dioxide concentrations (0

kPa CO2: © and full lines; 20 kPa CO2: 4 and dotted lines). Overall model #3 was

applied to all respiration data together.

32 2.4 Discussion

of chopped butter lettuce were 74 to 86, 122 to 159, and 240 to 300 nmol

CO2 kg−1 s−1 at 1, 4.5 and 10 ◦C respectively. In our study we found lower

values of 34 to 56, 52 to 92, and 87 to 141 nmol CO2 kg−1 s−1 at 1, 5 and

9 ◦C respectively. For strawberry, the maximum oxygen consumption rate at a

reference temperature of 10 ◦C was equal to 242 nmol kg−1 s−1. Chambroy et al.

(1993); Renault et al. (1994); Hertog et al. (1999) obtained similar results of 250,

210 and 270 nmol kg−1 s−1 for ‘Pajaro’ and ‘Elsanta’ strawberries respectively.

In the reported experiments, respiration rates of fresh-cut butterhead lettuce

and strawberry were found to be influenced by temperature, carbon dioxide

and oxygen concentration.

2.4.1 Temperature effect

Temperature has been identified as the most important external factor influ-

encing respiration (Fonseca et al., 2002). Reducing the temperature from 9 ◦C

to 1 ◦C in our experiments, resulted in a reduction of the maximum O2 con-

sumption rate of fresh-cut butterhead lettuce with a factor 2.7. A comparable

temperature effect has been found by Krahn (1977) who reported that the res-

piration rate of fresh cut head lettuce at 5 ◦C was 40% higher than at 0 ◦C

and by Behrsing et al. (1998) for chopped butter lettuce. The latter authors

showed that the carbon dioxide production rates of chopped butter lettuce were

74 to 86, 122 to 159, and 240 to 300 nmol CO2 kg−1 s−1 at 1, 4.5 and 10 ◦C

respectively. In our study we found lower values as compared to Behrsing et al.

(1998) of 34 to 56, 52 to 92, and 87 to 141 nmol CO2 kg−1 s−1 at 1, 5 and 9 ◦C

respectively. The results of Varoquaux et al. (1996) for minimally processed

butterhead lettuce are in good agreement with our results. They found that the

O2 consumption rate after 6 days of storage at 1 or 10% O2 without CO2 at a

temperature of 8 ◦C was 138 nmol O2 kg−1 s−1. This value is comparable to

the O2 consumption rate we obtained for fresh-cut butterhead lettuce stored at

9 ◦C and at atmospheres containing at least 2 kPa O2 and no carbon dioxide.

In the overall models, the temperature effect was described by an Arrhe-

nius equation. The effect of temperature on the oxygen consumption rate is

reflected in EaVmO2 which was equal to 85 kJ mol−1. This value lies in the

range normally found for EaVmO2 in vegetable produce (around 40 to 110 kJ

mol−1) (Fonseca et al., 2002). The effect of temperature on the carbon dioxide

production rate is reflected in EaVmCO2 which was equal to 50 kJ mol−1. This

value is in accordance with the value of 38 kJ mol−1 which was calculated from

the results of Behrsing et al. (1998) for chopped butter lettuce shown above.

For strawberry the parameter EaVmO2 was equal to 64 kJ mol−1. This value

Analysis and modelling of high oxygen effects on respiration 33

is in good agreement with the estimate obtained by Hertog et al. (1999) and

Chambroy et al. (1993) which was 74 and 65 kJ mol−1 respectively.

2.4.2 Carbon dioxide effect

An increase in CO2 level from 0 to 10 kPa (fresh-cut butterhead lettuce) or 20

kPa (strawberry), resulted in a decrease of the respiration rate. The respiratory

inhibition of CO2 has been hypothesized as a simple feedback inhibition (Herner,

1987) or as an inhibiting effect of CO2 on Krebs cycle intermediates and enzymes

(Kader et al., 1989). Others considered that CO2 might inhibit C2H4 production

and action rather than having a direct effect on the respiration process (Fonseca

et al., 2002; Mathooko, 1996). A further increase of the CO2 concentration to

20 kPa, resulted in similar respiration rates of fresh-cut lettuce as those at 0

kPa CO2. The increased respiration rate at 20 kPa CO2 as compared to 10

kPa CO2 is probably due to an injury response (Fonseca et al., 2002). Some

varieties of lettuce are very sensitive to CO2, and brown stain is a common CO2

injury when the product is exposed to levels above its tolerance limit (Kader

et al., 1989). CO2 levels above 10 kPa are generally considered as injurious for

butterhead lettuce (Lopez-Galvez et al., 1996; Gorny, 2001). Additionally, the

carbon dioxide tolerance limit for fresh-cut produce is known to be lower than

for uncut produce (Gorny, 2001). Varoquaux et al. (1996) also found an increase

in respiration activity when butterhead lettuce was stored in a CO2 enriched

(10 or 20 kPa) atmosphere. The increase was dependent on the cultivar and

the duration of exposure to carbon dioxide. Also carrots exhibited a decrease

in respiration rate at 10% CO2 and an increase at 30% CO2 (Pal and Buescher,

1993). Smyth et al. (1998) found no effect of carbon dioxide concentration on

the respiration rate of cut iceberg lettuce. About the effect of carbon dioxide on

strawberry respiration, some controversy exists. Some authors found no effect of

CO2 (Hertog et al., 1999) on the respiration whereas others did (Li et al., 1989;

Talasila et al., 1992). In our study, there was significant inhibitive influence of

20% kPa CO2. This influence however, is smaller than the temperature effect

and the effect of very low oxygen concentrations.

For both products, the model in which the inhibitive effect of CO2 was

described by an uncompetitive inhibition term (overall model #1) could not

be distinguished from the model with a noncompetitive CO2 inhibition term

(overall model #2). It was suggested by Peppelenbos and van ’t Leven (1996)

that the linear mixed inhibition equation was most closely related to what is

actually occurring in plant tissues. However, the noncompetitive type showed

good results on a range of tested products too (Peppelenbos and van ’t Leven,

34 2.4 Discussion

1996; Lammertyn et al., 2001), and can be considered more desirable because

of its simplicity as compared to the linear mixed inhibition type. In a number

of publications, however, authors could not statistically distinguish between

different inhibition types (Fonseca et al., 2002), as is also the case in our study.

2.4.3 Effect of low oxygen concentrations

Our results indicated that oxygen concentrations should be below 2 kPa to

obtain a significant reduction in the respiration rate for fresh-cut butterhead

lettuce. This was reflected in a low KmO2 value (1.1, 1.0 and 0.9 kPa result-

ing from fitting overall model #1, #2 and #3 respectively). Varoquaux et al.

(1996) indicated that the oxygen concentration has to be even lower than 1%

to significantly affect the respiration rate of fresh-cut butterhead lettuce, since

comparable respiration rates were obtained at 1 and 10% oxygen. For shredded

iceberg lettuce even lower KmO2 values (0.26 or 0.19 kPa) were reported (Smyth

et al., 1998). In addition, also the fermentation induction point estimated by

the latter authors was much lower (0.3 to 0.8 kPa, depending on the tempera-

ture) than estimated from our experiments (2.2 kPa). This can be due to the

higher oxygen sensitivity of butterhead lettuce as compared to iceberg lettuce.

For fresh-cut butterhead lettuce oxygen concentrations below 1% are consid-

ered as injurious whereas for other varieties the oxygen concentration may be

as low as 0.5% before injury occurs (Gorny, 2001). Additionally, the lack of

respiration measurements between 0 and 2 kPa hampers an accurate estimation

of parameters as the KmO2 value and parameters related to fermentation pro-

cesses. This can also be seen from the relatively large 95% confidence intervals

of the fermentation-linked parameters EaVmCO2 and KmO2f .

Renault et al. (1994) found no effect of oxygen concentrations between 2 and

20 kPa on the oxygen consumption of ‘Selva’ strawberries. This is in agreement

with our results and is reflected in a small KmO2-value for ‘Elsanta’ strawberries

of 1.2 kPa. Small changes in oxygen concentration between 0 and 2 kPa will

result in large changes in the oxygen consumption. The KmO2-value estimated

by Hertog et al. (1999) for ‘Elsanta’ strawberries was 2.63% and thus higher than

in our experiments. Differences in KmO2-values might be related to differences

in strawberry size. Lammertyn et al. (2001) found that the KmO2-value for

intact pears was approximately 1000-fold larger than the one for protoplasts in

suspension, since the first also includes diffusion properties. Analogously, the

contribution of diffusion properties to the Michaelis-Menten constant might be

greater in large fruits compared to small fruits. The estimated rqox from our

experiments is 0.66 which is low in comparison to the results of Hertog et al.

Analysis and modelling of high oxygen effects on respiration 35

(1999) and Renault et al. (1994) where it is 0.91 and 1 respectively. The reason

for discrepancies in measured rq values can be related to the applied method for

respiration measurement which will be explained more extensively in Section

2.4.5.

2.4.4 Effect of high oxygen concentrations

We found that superatmospheric oxygen concentrations had no effect on the

oxygen consumption and carbon dioxide production of ‘Elsanta’ strawberries.

Respiration rates at oxygen concentrations above 20 kPa were comparable to

those at 20 kPa. Consequently, a Michaelis-Menten model was applicable also

when including respiration rates at high oxygen concentrations. Wszelaki and

Mitcham (2000) measured the carbon dioxide production rates of ‘Camarosa’

strawberries stored for 7 days at 20, 40, 60, 80, 90 or 100 kPa O2. After 1 day, the

CO2 production rate at 20 kPa O2 was significantly higher than at the higher

O2 concentrations. In comparison to our results, this indicates that not all

strawberry cultivars respond similarly to high oxygen concentrations. However,

between 3 and 5 days of treatment the fruit in the high O2 treatments began

to respire more rapidly. By 7 days of treatment, fruit from all treatments with

≥ 40 kPa O2 had a greater respiration rate than fruit stored in air. In our case

respiration of strawberries was only measured during three days of treatment.

Analogous to the results of Wszelaki and Mitcham (2000), the respiration rates

stayed constant during the first three days of storage.

In leafy organs, however, the influence of high oxygen concentrations is not

obvious as was stated in the introduction. We found a small inhibiting effect

of superatmospheric oxygen concentrations on the respiration rate of fresh-cut

lettuce. An overall model not including the high oxygen inhibitive effect pre-

dicted the respiration rates equally well as an overall model that did include

this effect.

The inhibitive effect of superatmospheric oxygen concentrations was included

as a noncompetitive inhibition term. Although KmO2i (the parameter describing

noncompetitive oxidative inhibition at superatmospheric oxygen conditions) was

significant in overall model #1 and #2, removal of the inhibition factor rO2i

did not affect the goodness-of-fit of the overall model (overall model #3). To

our opinion, overall model #3 has to be preferred because it has the same

descriptive power as overall models #1 and #2 while employing one parameter

less. A reason for the small effect of the rO2i parameter is that the high O2

inhibition on lettuce respiration seemed to be temperature dependent. Whereas

oxygen consumption rates at 5 and 9 ◦C were clearly inhibited at high oxygen

36 2.4 Discussion

concentrations, the influence at 1 ◦C was not obvious. Since the overall model

did not account for this interactive effect, an ‘intermediate’ rO2i parameter was

estimated which was not really appropriate to describe the high O2 inhibition

effect at 5 and 9 ◦C as can be seen from Figure 2.9.

Because of the absence (for strawberry) or only small inhibitive effect (for

fresh-cut butterhead lettuce) of superatmospheric oxygen concentrations on the

respiration rates, they provide no valuable alternative to very low oxygen con-

centrations in terms of respiration rate reduction. Therefore, when applying

superatmospheric oxygen concentrations e.g. to reduce enzymatic browning of

fresh-cut butterhead lettuce or reduce fungus growth on strawberries, control of

the respiration rate will fully depend on good temperature control and elevated

carbon dioxide levels up to 10 kPa (lettuce) or 25 kPa (strawberry).

2.4.5 Influence of respiration measurement method on rq

The method applied for respiration rate measurements has an influence on the

measured respiratory quotient (rq) values, which originates from differences in

measured CO2 production rates. In open or flow respiration systems, product

samples are placed in a sealed container through which a gas of known compo-

sition is led. O2 and CO2 respiration rates are determined by measuring the

change in O2 and CO2 concentration from the inlet to the outlet of the respi-

ration chamber. Since the gas composition is kept constant in the respiration

chamber, steady-state respiration rates are obtained and respiration rates are

fully determined by the measured change in O2 and CO2 concentration (under

the assumption that the produce is at equilibrium with the surrounding atmo-

sphere before measurements start). As a consequence, rq values measured using

an open system are close to the real rq values and should be close to 1, in case

hexose metabolisation and no fermentation is assumed (Lencki, 2004).

On the other hand, the experimental setup for open systems is rather com-

plex, and the system has the limitation of not being sufficiently accurate to

determine low respiration rates (Fonseca et al., 2002). In practice, closed respi-

ration chambers are more commonly used to determine respiration rates. The

experimental set-up is less complex but respiration rates are determined at

unsteady-state conditions. In systems where gas exchange is not measured

under steady state conditions, the measured gas exchange rate can be differ-

ent from the respiration rate at cellular level (de Wild and Peppelenbos, 2001).

At unsteady-state conditions, the proportion of CO2 that dissolves in the liq-

uid phase of the product becomes significant, whereas the O2 solubility can

be neglected (Lencki et al., 2004). CO2 in the liquid phase can be present as

Analysis and modelling of high oxygen effects on respiration 37

undissociated CO2, the hydrated form carbonic acid (H2CO3), and the ionized

forms HCO−3 and CO2−

3 at concentrations which are dependent on the pH of

the produce. Lencki et al. (2004) proposed the following equation to calculate

the rq (rqmeas) as it would be measured in a closed system:

rqmeas= rqreal

[1 +

RTVp

VfreeKCO2

(1 + K1 +

K1K2

[H+]+

K1K2K3

[H+]2

)]−1

(2.17)

where rqrealis the real respiratory quotient of the produce, R is the ideal

gas constant, T is the temperature, Vp is the produce volume, Vfree is the

free volume in the jar, KCO2 is Henry’s law constant for CO2, K1 is the rate

constant for the formation of carbonic acid from liquid CO2, K2 and K3 are the

dissociation constants for H2CO3 and HCO−3 , respectively.

From equation (2.17) it can be seen that in unsteady-state systems, the

measured rq is dependent on the produce loading (Vp/Vfree) and internal pH.

Also, the time between initial and final measurement will influence the measured

rq, and the calculation of rqmeas from equation (2.17) is representing the initial

rqmeas. For cut rutabaga, Lencki et al. (2004) found initial rq values ranging

from 0.8 to 0.9, which reached values above 1.0 within hours. The increase

as a function of time is the result of a pH rebalancing metabolism (malic acid

metabolism, which produces OH−, pyruvate and CO2), which is onset by an

initial internal pH drop because of CO2 solubilisation.

The values of rqmeaswere calculated for the respiration experiments on straw-

berry and fresh-cut butterhead lettuce according to equation (2.17). Values for

the dissociation constants (K1 = 1.2 10−3, K2 = 1.7 10−4 and K3 = 4.7 10−11)

were taken for pure water at 20 ◦C according to Lencki et al. (2004). The pro-

duce loading (Vp/Vfree) was 0.16 for cut lettuce and 0.46 for strawberry. An

internal pH of 6 and 3.5 was assumed for cut lettuce and strawberry, respec-

tively. Values for Henry’s constant in pure water are known. However, in a

system with a high solute concentration such as plant cells, the solubility of

CO2 is much smaller. In their calculations, Lencki et al. (2004) obtained better

prediction of rqmeasvalues when using 7000 kPa l mol−1 as value for Henry’s

constant in a cell solution at 20 ◦C (instead of 2590 kPa l mol−1 in pure water at

20 ◦C). When we used the value of 4668 kPa l mol−1 for KCO2 (calculated from

the value of 7000 kPa l mol−1 for a reference temperature of 5 ◦C according to

Zheng et al. (1997)), rqmeaswas equal to 0.91. The value of rqox as estimated

by applying the overall respiration model for fresh-cut lettuce at a reference

temperature of 5 ◦C was 0.78 as can be seen in Table 2.4.

Calculations were also done for strawberry. For KCO2 equal to 5368 kPa l

38 2.5 Conclusion

mol−1 (calculated according to Zheng et al. (1997) for a reference temperature

10 ◦C) the value for rqmeas was 0.83. The estimated rqox value for strawberry

was 0.66 (see table 2.5).

Differences in calculated rqmeas values using equation (2.17) and measured rq

values result from the fact that equation (2.17) is still a simplification of the real

situation. Firstly, the equation does not take into account the time dependence

of the apparent rq value in a closed system. Second, diffusion properties are not

taken into account. E.g., the permeability of the product skin and the porosity

of the tissue have a great influence on the O2 built up in the cell and the CO2

release to the external atmosphere (de Wild and Peppelenbos, 2001). If CO2

is retained in the cell due to diffusion limitations, the measured rq values will

further decrease.

All together, the measurement should reflect the real situation of a MAP ap-

plication as much as possible. In MA packages, the same phenomenon of CO2

solubilisation as in closed systems happens in the first few hours after packag-

ing when steady state is not reached (Renault et al., 1994). On the other hand,

when applying respiration rates measured at unsteady state conditions, CO2

production will be underestimated when the package atmosphere is at steady

state. When determining respiration rates using a permeable system however,

other problems do occur. First, more variables are involved such as package

dimensions and permeability characteristics, which are often difficult to deter-

mine precisely (Fonseca et al., 2002). Also, in permeable systems, respiration

rates are calculated based on O2 and CO2 concentrations at steady state condi-

tions Beaudry et al. (1992). Considerable time may be needed to reach steady

state conditions (Beaudry et al., 1992), which slows down the respiration mea-

surement and implies analysis on aging produce and possibly respiration by

micro-organisms. Additionally, only using steady state measurements, neglects

the initial unsteady state condition in the package, which can - as stated before

- be considerably long.

We can conclude that different measurement techniques exist to determine

respiration rates, all of them having specific (dis)advantages. When applying a

certain technique, one should be aware at what points the measured respiration

rates will differ from those in a real MA package.

2.5 Conclusion

The effect of oxygen (low and superatmospheric concentrations), carbon diox-

ide and temperature on the oxygen consumption and carbon dioxide production

Analysis and modelling of high oxygen effects on respiration 39

of ‘Elsanta’ strawberries and ‘Zendria’ fresh-cut butterhead lettuce, was eval-

uated. Temperature, carbon dioxide and oxygen concentrations below 2 kPa

significantly influenced the respiration rate of both products. Models based

on Michaelis-Menten kinetics were constructed. Temperature influences on the

maximum specific respiration rate of oxygen or carbon dioxide were described

according to Arrhenius. Carbon dioxide concentrations of 10 kPa inhibited the

respiration of fresh-cut butterhead lettuce, whereas respiration rates at 20 kPa

CO2 were comparable to respiration rates at 0 kPa CO2. For strawberry, car-

bon dioxide concentrations up to 20 kPa inhibited the respiration rate. The

inhibitive effect of carbon dioxide could be described in the model by a non-

competitive or an uncompetitive inhibition term equally well. Respiration of

strawberries stored at superatmospheric oxygen concentrations was comparable

to respiration at atmospheric oxygen levels. On the other hand, high oxygen

levels did have a small inhibitive effect on the respiration of fresh-cut butterhead

lettuce. To model this effect, a noncompetitive high oxygen inhibition term was

included. However, because of the only minor inhibitive effect of superatmo-

spheric oxygen concentrations on the respiration, a model not including the high

oxygen inhibition term described the gas exchange equally well as a model that

included the effect. The constructed overall models described the respiration

data of strawberry and fresh-cut butterhead lettuce well, as based on the high

R2adj values of 0.96 and 0.82 respectively.

40 2.5 Conclusion

Chapter 3

Analysis and modelling of

high oxygen effects on

microbial growth

3.1 Introduction

Besides stringent temperature control, modified atmosphere packaging (MAP)

can be used to suppress the growth of undesired micro-organisms on fruit and

vegetable produce. An inhibitive effect of high oxygen on mould growth was

reported by several authors. Hoogerwerf et al. (2002) found that the colony

diameter of Rhizopus stolonifer, Botrytis cinerea and Penicillium discolor on

malt extract agar at 80% O2 was respectively 94%, 83% and 96% of that in air

after an incubation time of respectively 41 h, 70 h and 136 h. Wszelaki and

Mitcham (2000) found no in vitro mycelial growth of B. cinerea at 100% O2,

whereas in air it doubled from day 5 to day 14. The duration before visual

appearance of mycelium on agar was doubled at 95% O2 compared to at 21%

O2 for Aspergillus flavus and was three times as high in the case of B. cinerea.

According to Perez and Sanz (2001), commercial loss of strawberries because of

B. cinerea growth was 0% under atmospheres with 90% O2 + 10% CO2 or 80%

O2 + 20% CO2. Losses climbed up to 11% under 5% O2 + 20% CO2 and 62%

under air conditions.

However, there is a considerable variability in sensitivity among different

strains (Allende et al., 2002; Van der Steen et al., 2002). Stronger and more

consistent inhibition of microbial (not only mould but also bacterial) growth

41

42 3.1 Introduction

is obtained when high oxygen concentrations are combined with elevated car-

bon dioxide concentrations (Amanatidou et al., 1999; Kader and Ben-Yehoshua,

2000; Van der Steen et al., 2003). According to Day (2001) high oxygen MAP

treatments have been found to inhibit total aerobes, total anaerobes, yeasts,

moulds, Pseudomonas species, Enterobacteriaceae and coliforms. Also here, the

antimicrobial effects observed were the combined effect of high O2 and generated

CO2.

The major cause of O2 toxicity is the formation of reactive oxygen species

(ROS) such as the superoxide anion radical (O•−2 ), hydrogen peroxide (H2O2)

and the hydroxyl radical (HO•) which destroy several cellular components, ei-

ther by (photo)chemical reactions in the cellular environment, or by endogenous

cellular enzymes (Kader and Ben-Yehoshua, 2000). Micro-organisms dispose of

several defense systems to counter oxidative stress, such as H2O2 decompos-

ing enzymes (peroxidases, catalases), O•−2 decomposing enzymes (superoxide

dismutases) or radical scavengers. The variability in suppression by high O2

among micro-organisms can be explained by the differences in endogenous ROS

production and in the presence and efficiency of different defense systems.

As compared to the numerous studies on the effect of high carbon dioxide

- low oxygen atmospheres, information about the effect of high oxygen atmo-

spheres on bacterial growth is relatively scarce. To avoid variations in gas atmo-

sphere, in food compositions and presence of endogenous micro-organisms, high

oxygen modified atmosphere experiments with pure bacterial cultures on solid

surface laboratory medium have been performed (Ogihara et al., 1993; Ama-

natidou et al., 1999; Jacxsens et al., 2001; Van der Steen et al., 2003). In all

cases only a few gas combinations were tested and data analysis was restricted

to a description of the obtained microbial growth curves.

Therefore, the objective of the present research was to study the effect of

high O2 and elevated CO2 levels on the growth of Pseudomonas fluorescens and

Listeria innocua quantitatively, in order to analyse the effect of both gases and

to develop a predictive growth model. Pseudomonas fluorescens was used as a

model organism for bacterial spoilage. Pseudomonas fluorescens is a vegetable-

associated micro-organism which can have pectinolytic activity, thus causing

bacterial spoilage of the host product. A lot of Pseudomonas strains have a

psychrotrophic nature, making them important in refrigerated products such as

vegetables or fresh-cut products. Listeria innocua was used as model organism

for the pathogen Listeria monocytogenes. Several studies have demonstrated

that the behaviour of both organisms is comparable as affected by temperature,

acidification and modified atmosphere (Hugas et al., 1998; Thomas et al., 1999).

Analysis and modelling of high oxygen effects on microbial growth 43

It is known that MAP can favour the growth of some pathogens due to the

elimination of natural competitors and the associated prolongation of the shelf

life (Bennik et al., 1995; Hugas et al., 1998; Francis et al., 1999). Pathogens

that survive at low temperatures and anoxic situations are of special interest

(Francis et al., 1999). One of these pathogens is the psychrotrophic, facultative

anaerobic bacterium Listeria monocytogenes, which can be found in a variety

of foods and causes listeriosis. The growth models will be based on the growth

on a solid agar surface, since many products are mainly contaminated at the

surface (Bennik et al., 1995). The growth models will be validated on fresh-cut

butterhead lettuce. Growth will be studied at a temperature of 7 ◦C, being the

maximum allowed temperature for storage of refrigerated products in Belgium

(Anonymous, 1982).

This chapter is divided in four main sections. In Section 3.2 the materials

and methods are described. The section explains which bacterial strains and

methods were used for experiments on artificial growth substrate (in vitro) and

on fresh-cut butterhead lettuce (in vivo). The applied techniques for predictive

modelling and model validation are described as well. Results are given in Sec-

tion 3.3. First, the results of the in vitro experiments on both micro-organisms

are presented. Second, predictive growth models are constructed. Third, the

predictive models are validated based on additional in vitro experiments and

experiments on fresh-cut butterhead lettuce. In Section 3.4 the results are dis-

cussed and compared to literature data. Concluding remarks are formulated in

Section 3.5. The results presented in this chapter are published in Geysen et al.

(2005a,d, 2006a).

3.2 Materials and Methods

3.2.1 Bacterial strains and inoculation of growth substrates

3.2.1.1 In vitro experiments

Pseudomonas fluorescens strain LMG 7207 (isolated from rotting leaves of Ci-

chorium endivia var. latifolium red cv.), obtained from the BCCMTM/LMG

Culture Collection (Gent, Belgium) and Listeria innocua Seeliger 1983 strain

CIP 80.12 (isolated from faeces), obtained from the Institut Pasteur (Paris,

France), were used. Bacteria were stored at −80 ◦C in nutrient broth (NB, Ox-

oid, Hampshire, England) supplemented with 25% glycerol. Two subsequent

subcultures were grown in nutrient broth at 30 ◦C (Pseudomonas fluorescens)

or 37 ◦C (Listeria innocua) until stationary phase, for 24 h (first subculture)

44 3.2 Materials and Methods

and 21 h (second subculture). The second subculture was then kept at 7 ◦C

for 4 h to allow cold adaptation. From the second subculture a dilution series

was made in PPS (Peptone Physiological Salt containing 8.5 g l−1 NaCl and 1

g l−1 bacteriological peptone (L37, Oxoid)). 100 µl from the appropriate dilu-

tion was spread on nutrient agar plates with a diameter of 9 cm, to obtain a

population density of 103-104 cfu cm−2 agar plate. Nutrient agar was chosen

for these experiments because it is less nutritious than most other commonly

used non-selective growth media such as tryptic soy and brain heart infusion

agar, and thus probably is more representative for the conditions prevailing on

vegetables. The growth ability of the selected strains on nutrient agar at 7 ◦C

under ambient atmosphere was confirmed in preliminary tests (data not shown).

Petridishes with ventilation ribs were used to ensure good gas exchange.

3.2.1.2 In vivo experiments

For the experiments on fresh-cut butterhead lettuce, the same Listeria innocua

strain as for the in vitro experiments (Listeria innocua Seeliger 1983 strain CIP

80.12) was used. In order to follow the growth of Pseudomonas fluorescens on

cut lettuce, which is strongly contaminated with several Pseudomonas-species,

GFP (Green Fluorescent Protein) tagged mutants of Pseudomonas fluorescens

strain LMG 7207 were developed. To obtain these mutants, a triparental mating

was set up with an Escherichia coli donor strain carrying pBK-miniTn7-gfp1

and an E. coli helper strain carrying pUX-BF13 as described by Koch et al.

(2001). The pBK-miniTn7-gfp1 plasmid carries a Tn7-based transposon, which

encodes both kanamycin and chloramphenicol resistance, and green fluorescent

protein under the control of a constitutive lac promoter, while the pUX-BF13

plasmid provides the Tn7 transposition function in trans (Koch et al., 2001).

During conjugation both plasmids are introduced into the recipient P. fluo-

rescens, allowing transposition of the Tn7 derivative to occur. The Tn7 trans-

poson has the preference to insert near the glmS locus without polar effects,

while the delivery plasmids can not be stably maintained by P. fluorescens. As

a result of this procedure, several P. fluorescens mutants were isolated that

are resistant to kanamycin and chloramphenicol, and constitutively express the

GFP-gene.

The growth ability of the GFP tagged mutants was verified in nutrient broth

(NB, Oxoid) at 30 ◦C and on petridishes with nutrient agar at 7 ◦C at different

gas conditions (air, 100% O2 and 87.5% O2 + 12.5% CO2) and was found

identical to that of the untransformed strain (data not shown). Storage and

preparation of the bacterial suspension was done in the same way as for the in

Analysis and modelling of high oxygen effects on microbial growth 45

vitro experiments.

Butterhead lettuces (Lactuca sativa L. cultivar ‘Zendria’ or ‘Flandria’) grown

in a commercial greenhouse in Mechelen (Belgium) were used for inoculation

with the bacterial strains. The lettuce heads were processed in a clean room at

7 ◦C, after soiled and decayed external leaves were eliminated. First, the central

stem was removed and the lettuce was hand cut into pieces of about 2 cm using a

sharp stainless steel knife. The cut lettuce was immediately immersed for 1 min

in tap water at 5 ◦C and centrifuged using a domestic centrifuge, which removed

excess water without causing any visible damage to the lettuce. Lettuce samples

of 120 g were put in a glass jar and were inoculated with 5 ml of the appropriate

dilution of bacterial suspension (Listeria innocua or GFP tagged P. fluorescens

mutants) to reach an inoculum density of 103-104 cfu g−1.

3.2.2 Experimental setup

3.2.2.1 In vitro experiments

Inoculated agar plates were incubated in glass jars in a cool room set at 7 ◦C.

Temperature was monitored every 30 minutes with temperature loggers (Escort

Junior, Techninnovators, New Zealand). The actual temperature in the cool

room was 6.7 ± 0.3 ◦C. Inoculated agar plates at room temperature needed

1.5 h to reach a temperature of 7 ◦C. Pure gases (oxygen, carbon dioxide and

nitrogen) were mixed using mass-flow controllers (model 5850S, Brooks Instru-

ment, The Netherlands). The obtained gas mixtures were humidified by passage

through a water bottle. The humidified gas was then introduced in a series of

jars containing the agar plates at a flow rate of 10 l h−1. Gas concentrations

inside the jars were checked using a micro-GC (CP2003-P, Varian-Chrompack,

Bergen op Zoom, The Netherlands), and were found to be constant during the

experiment and identical in all jars within a series. The tested gas conditions

were selected using a mixture design (Cornell, 1981). The reason for this choice

is that classic designs such as, for example, a full factorial design, are less suit-

able when applying oxygen concentrations up to 100%. For example, an oxygen

concentration of 90% can only be combined with carbon dioxide levels up to

10%. In the presented study, mixture theory was only used to define the exper-

imental conditions, and was not used in further evaluation of the data because

nitrogen has no effect on microbial growth, thus only leaving oxygen and carbon

dioxide to be considered.

The principle of mixture theory is that the sum of the proportions of the com-

pounds (in our case O2, CO2 and N2) is one. Mixtures with three compounds

46 3.2 Materials and Methods

O2

N2

(20, 25, 55)(20, 12.5, 67.5)

(20, 0, 80)

(47.5, 25, 27.5)

(53.75, 12.5, 33.75)(60, 0, 40)

(100, 0, 0)

(87.5, 12.5, 0)

(75, 25, 0)

O2 between 20 and 100%

CO2

CO2 between 0 and 25%

Figure 3.1: Mixture design for the selection of tested gas conditions. The region of

interest is marked in grey and is limited in oxygen between 20% and 100% and carbon

dioxide between 0% and 25%. The tested gas mixtures for identification of the models

are marked with crosses (×). The corresponding gas concentrations are given between

brackets in the following order: (% O2, % CO2, % N2).

can be presented in a two-dimensional triangle, the corners of the triangle rep-

resenting pure gases. In the middle of the triangle, the three gases are present

in equal proportions. An experimental region with the following constraints was

chosen: CO2 between 0% and 25% and O2 between 20 and 100%, resulting in

a sub region of the whole factor space (see Figure 3.1). Carbon dioxide concen-

trations higher than 25% were not included because of their detrimental effect

on quality for the majority of fresh fruit and vegetable products (Herner, 1987).

A total of nine gas combinations were selected as based on the mixture design

(% O2, % CO2, % N2) : (100, 0, 0) ; (87.5, 12.5, 0) ; (75, 25, 0) ; (60, 0, 40) ;

(53.75, 12.5, 33.75) ; (47.5, 25, 27.5) ; (20, 0, 80) ; (20, 12.5, 67.5) ; (20, 25, 55).

3.2.2.2 In vivo experiments

Glass jars with inoculated cut lettuce were put in a cool room set at 7 ◦C. Gas

mixtures were prepared and monitored as for the in vitro experiments. For

the experiment with the GFP tagged Pseudomonas fluorescens mutant, the

humidified gas mixtures were introduced in a series of 4 jars with inoculated cut

lettuce at a flow rate of 10 L h−1. The following five gas conditions were applied

(% O2, % CO2, % N2) : (20, 0, 80) ; (20, 15, 65) ; (5, 15, 80) ; (75, 0, 25) ; (75,

15, 10). The growth of Listeria innocua on fresh-cut butterhead lettuce was

Analysis and modelling of high oxygen effects on microbial growth 47

monitored in two independent in vivo experiments. In both experiments, 3 jars

filled with inoculated cut lettuce were used per gas condition. In experiment 1,

five gas conditions were applied (% O2, % CO2, % N2) : (20, 0, 80) ; (5, 0, 95) ;

(5, 15, 80) ; (75, 0, 25) ; (75, 15, 10). In experiment 2 the applied gas conditions

were the same as for the in vivo Pseudomonas fluorescens experiment.

3.2.3 Enumeration of viable cells

3.2.3.1 In vitro experiments

On each sampling time, 3 replicate plates per gas condition were analysed. The

content of each agar plate was emptied aseptically in a stomacher bag. PPS

was added until a total volume of 100 ml was reached. The sample then was

homogenised in a masticator (IUL Masticator Basic 0470, IUL S.A., Spain).

Appropriate dilutions were plated on nutrient agar and colonies were counted

after 24 h of incubation at 30 ◦C (Pseudomonas fluorescens) or after 48 h of

incubation at 37 ◦C (Listeria innocua). Eight to forteen sampling times per

gas condition were chosen, based on the results of a preliminary experiment

(results not shown), in order to obtain data in all stages of microbial growth. A

count of appropriate dilutions of the used inoculum suspension was carried out

to calculate the initial population density in the plates.

3.2.3.2 In vivo experiments

For the experiment with the GFP tagged P. fluorescens mutant two samples

of 30 g of inoculated cut lettuce per gas condition were taken after 0, 1, 2,

3, 6, 8 and 10 days of storage. The samples of 30 g of cut lettuce were put

aseptically in a stomacher bag. 270 ml PPS was added. The sample then was

homogenized in a masticator (IUL Masticator Basic 0470, IUL S.A., Spain).

Appropriate dilutions were plated on Pseudomonas CFC agar which consists

of Pseudomonas agar base (CM0559, Oxoid) supplemented with CFC selective

agar supplement containing cetrimide, fucidin and cephalosporin (SR0103, Ox-

oid). Colonies were counted after 48 h of incubation at 30 ◦C. Total counts of

Pseudomonas species were performed under white light. To distinguish colonies

of the GFP tagged P. fluorescens mutant, the plates were examined using a

Lightools Illuminated Tunable Lighting System (Lightools, California, USA)

equipped with an emission filter (GFP 515 nm high pass viewing glass) and

excitation filter (470 nm/40 nm low pass filter cup) adapted for visualisation of

GFP. The device is shown in Figure 3.2. In Figure 3.3 Pseudomonas colonies

plated on Pseudomonas CFC agar viewed under white light and under filtered

48 3.2 Materials and Methods

Figure 3.2: Lightools Illuminated Tunable Lighting System (Lightools, USA).

Figure 3.3: Pseudomonas colonies (GFP transformed and non-transgenic) on Pseu-

domonas CFC agar viewed under white light (right) and under filtered light (470 nm)

through a 515 nm viewing glass (left).

light (470 nm) through a 515 nm viewing glass are shown. Using the latter

system, GFP tagged P. fluorescens colonies can be clearly distinguished from

non-transgenic colonies by their green fluorescent appearance. To analyse the

aerobic mesophilic bacteria of the samples, 1 ml aliquots were pour plated on

plate count agar (PCA, CM0325, Oxoid) and incubated for 48 h at 30 ◦C.

For the first in vivo experiment with L. innocua three samples of 30 g of

inoculated cut lettuce per gas condition were taken after 0, 3, 5, 7 and 10 days of

storage. For the second experiment, two samples per gas condition were taken

at day 0, 2, 3, 7, 9 and 10. Homogenisation and dilution was carried out as in the

P. fluorescens experiment. Appropriate dilutions were spread plated on Listeria

selective agar prepared from Listeria selective agar base (CM 0856, Oxoid) and

Listeria selective supplement (SR0140, Oxoid). As a control, samples taken

Analysis and modelling of high oxygen effects on microbial growth 49

from non-inoculated cut lettuce were also plated on Listeria selective agar. The

Listeria selective medium utilises the indicator system aesculin and ferrous iron

for the differentiation of Listeria monocytogenes. This bacterium hydrolyses

aesculin, producing black zones around the colonies due to the formation of

black iron phenolic compounds derived from the aglucon. In a preliminary test,

it was confirmed that also Listeria innocua strain CIP 80.12 produced black

zones indicating aesculin hydrolysation. Diluted samples of the first in vivo

experiment were also plated on PCA (CM0325, Oxoid) and incubated for 48

h at 30 ◦C to analyse the total aerobic mesophilic count. Additionally, lactic

acid bacteria were analysed by pur plating on MRS agar (de Man, Rogosa

and Sharpe, CM0361, Oxoid) with a top layer and incubation for 4 days at

25 ◦C. Lactic acid bacteria were analysed during the in vivo experiment on L.

innocua since they may exert antimicrobial effects due to e.g. the production of

antilisterial compounds, which are bacteriocins with an action against Listeria

species (Francis et al., 1999).

3.2.4 pH measurements

To determine differences in pH change between nutrient agar and fresh-cut but-

terhead lettuce, the pH of nutrient agar and fresh-cut butterhead lettuce was

monitored after 0, 3, 7 and 10 days of storage at 20% O2 combined with 0, 12.5

or 25% CO2. 10 g of cut lettuce was homogenised in a small blender (Waring

commercial, Torrington, USA) together with 50 mL distillated water. To mea-

sure the pH, the glass electrode of the pH meter (744 pH meter, Metrohm Ltd.,

Switzerland) was inserted in the nutrient agar and the lettuce homogenate.

3.2.5 Model development

We used three steps to obtain a model for the influence of time, oxygen and car-

bon dioxide on the growth of Pseudomonas fluorescens and Listeria innocua.

The first step was the construction of a primary model which describes the bac-

terial growth as a function of time. Secondary models described the influence of

the gas composition on growth parameters resulting from primary models (µmax

and λ). These first two steps correspond to the standard two-step method that

is mostly used in predictive microbiology, as described in, for example, Whiting

(1995). In an additional third step the secondary models were integrated in the

primary model and the model parameters were re-estimated. This so-called one-

step regression approach has been applied by several researchers before (Willocx,

1995; Fernandez et al., 2002; Valdramidis et al., 2005) to prevent accumulation

50 3.2 Materials and Methods

of fitting errors.

3.2.5.1 Primary growth model

For primary modelling, the growth data were analysed with the currently widely

used Baranyi equation (Baranyi and Roberts, 1994). To fit the model to the

data, a program was written in Matlab® (The Mathworks, Inc., Natick, USA)

using the Optimization Toolbox. Reparameterised model equations as applied in

the program MicroFit version 1.0 (developed by the Institute of Food Research,

http://www.ifrn.bbsrc.ac.uk/MicroFit/ and described in equations (3.1),

(3.2) and (3.3) were used. The estimate for lag time (λ) was restricted to give a

positive outcome. The base 10 logarithm of the cell numbers, n(t), is given by:

n(t) = n0 +n1(t)ln(10)

− n2(t)ln(10)

(3.1)

where

n1(t) = µmaxt + ln(e−µmaxt − e−µmax(t+λ) + e−µmaxλ) (3.2)

and

n2(t) = ln(1 + 10(n0−nmax)(eµmax(t−λ) − e−µmaxλ)) (3.3)

The parameters of the model are n0 and nmax, the base 10 logarithm of the

initial and final cell numbers (log cfu cm−2), µmax, the specific growth rate (h−1)

and λ, the lag time (h). Note that, as in MicroFit, the cell numbers are rescaled

to base 10 logarithms, but that µmax and λ have the same meaning as in the

original equations (Baranyi and Roberts, 1994) in which the amount of bacteria

was expressed as natural logarithms of the bacterial counts. The parameters

and their corresponding approximate 95% confidence limits were estimated.

3.2.5.2 Secondary growth models

In the second stage, the estimates for µmax and λ were fitted by equations

describing the effect of the gas condition on µmax or λ.

Pseudomonas fluorescens For the secondary models of Pseudomonas flu-

orescens the square root of µmax and the natural logarithm of λ were actually

used as variance stabilising transformation in the modelling procedure, as based

on the analysis presented in Zwietering et al. (1994). Two pairs of candidate

secondary models were examined. In the first pair of models, the expressions

for µmax (equation (3.4)) and λ (equation (3.5)) were described using multiple

Analysis and modelling of high oxygen effects on microbial growth 51

linear regression on O2 and CO2 concentrations. O2 and CO2 concentrations

are given in percentages.

õmax = m + mO2O2 + mCO2CO2 (3.4)

ln(λ) = l + lO2O2 + lCO2CO2 (3.5)

The constants m, mO2 , mCO2 , l, lO2 and lCO2 are regression parameters.

Secondly, we investigated a simplification of the model based on the hypoth-

esis of a simple relationship between λ and µmax. As was suggested by Robinson

et al. (1998), the lag time can be defined as the ratio of the work needed to adapt

to a new environment and the rate at which the micro-organism is able to do

that work. If the rate to do the work is considered to be proportional to µmax,

and it is assumed to be constant for cells in the same initial state, one expects

a plot of µmax versus λ−1 to yield a straight line. Although in many studies

this assumption has proven to be an oversimplification (Robinson et al., 1998),

we found that for our dataset there was a linear relationship between λ−1 and

µmax, as will be shown further. This allowed us to build a model where µmax

is defined as in equation (3.4), and only one extra parameter (lm) was used to

define λ (equation (3.6)).

The lag phase was described as:

λ =lm

µmax(3.6)

where lm is a regression parameter.

Listeria innocua The variances of µmax and λ of Listeria innocua were anal-

ysed according to Zwietering et al. (1994) and were found to be independent of

the magnitude of µmax and λ. Consequently, no variance-stabilising transfor-

mations were applied.

For Listeria innocua the estimates for µmax were fitted by a Ratkowsky-

type model (Ratkowsky et al., 1982) (equation (3.7)) which describes the effect

of CO2 on µmax. Concentrations of CO2 are inserted as percentages.

µmax = m(CO2max − CO2) (3.7)

The constants m and CO2max are regression parameters. The effect of CO2

on the lag-phase of Listeria innocua was also described by equation (3.8).

λ =√

l CO2 (3.8)

52 3.2 Materials and Methods

Table 3.1: Overview of included terms in the Baranyi-equation (Eqn. (3.1)) to obtain

the overall models for P. fluorescens.

Overall model Lag phase Maximum specific growth rate

Model #1 Eqn. (3.5): Eqn. (3.4):

ln(λ) = l + lO2O2 + lCO2CO2√

µmax = m + mO2O2 + mCO2CO2

Model #2 Eqn. (3.6): Eqn. (3.4):

λ = lmµmax

õmax = m + mO2O2 + mCO2CO2

The constant l is a regression parameter. According to Ross (1993), the

parameter CO2max is the ‘notional’ maximum CO2-concentration. This means

that it does not have to be interpreted as a ‘true’ maximum, but as the CO2-

concentration at which the given parameter (µmax) becomes 0.

3.2.5.3 Overall growth models

After identification of the secondary models, the expressions for µmax and λ

were inserted in the Baranyi equation, which results in an ‘overall model’ as

it will be called below. The insertion of expression 3.7 for µmax and 3.8 for

λ in the Baranyi equation (equation (3.1)), results in the overall model for

Listeria innocua. In this way we obtain a non-linear equation with 5 parameters

(n0, nmax, m, CO2max, l). Two overall models for Pseudomonas fluorescens

were constructed. Overall model #1 is the Baranyi equation (3.1) where µmax

is described by equation (3.4) and λ by equation (3.5). In overall model #2

equation (3.4) and (3.6) are introduced in the Baranyi equation to describe

µmax and λ respectively. An overview of included terms in the overall models

for P. fluorescens is given in Table 3.1.

Overall model #1 and #2 for P. fluorescens use respectively 8 (n0, nmax,

l, lO2 , lCO2 , m, mO2 , mCO2) and 6 (n0, nmax, lm, m, mO2 , mCO2) parameters

to describe the growth. The model parameters were re-estimated, using the

parameter estimates of the inserted secondary models as the initial starting point

for the non-linear search procedure. The precision of the estimates is increased

because the method used all the raw growth data, whereas a standard secondary

model is built on estimates resulting from primary modelling. This one-step

regression method should, in general, result in a minimum overall deviation

between experimental and predicted values.

The application of one-step regression assumes that the underlying errors

have homogeneous variances. To determine the variation of the errors of the n

data as a function of the gas condition a Levene’s test (Levene, 1960) on the

Analysis and modelling of high oxygen effects on microbial growth 53

residuals resulting from the fit of equation (3.1) was performed. When errors

are found to have non-homogeneous variances, a weighted sum of squared errors

(WSSE) criterion needs to be implemented to fit the overall model equation to

the data.

WSSE =k∑

i=1

wi

li∑j=1

(noverall(xi, tij)− ncountij )2 (3.9)

with

wi =1

MSEi(3.10)

k is the number of gas conditions, li is the number of measurement points

of the ith gas condition. noverall(xi, tij) is the base 10 logarithm of the bacterial

count at the ith specific gas condition (xi) and time (tij) predicted with the

overall model and ncountij is the base 10 logarithm of the bacterial count at the

ith specific gas condition and time tij . MSEi is the mean squared error resulting

from the fit of equation (3.1) on the data of the ith specific gas condition.

For linear regression the GLM procedure of the software package SAS/STAT®

version 8.2 (SAS Institute Inc., Cary, North Carolina, United States) was ap-

plied. The (weighted) least squares optimisation is carried out using the Matlab

least squares non-linear optimisation routine (lsqnonlin) with the Levenberg-

Marquardt method. For primary as well as for overall modelling, the solution

for lag time was restricted to a positive value. The adjusted R2 and root mean

squared error (RMSE) were calculated. To assess normality of the residual

values, normal probability plots were made and evaluated in SAS/STAT®.

3.2.6 Model validation

In general the discrepancy between model prediction and observed microbial

growth in a system will gradually increase as the system becomes more com-

plex. When validating a model, different sources and types of errors can be

distinguished. To have more insight on the contribution of each factor on the

overall error, validation can be carried out on substrates with different levels

of complexity. When predicting bacterial responses under similar laboratory

conditions, the bias and inaccuracy of the model are called the primary bias

and primary error. The terms overall bias and overall error are used when the

model is validated under natural food conditions. The overall error includes the

primary error, the error due to growth substrate and the error due to microbial

diversity (Pin et al., 1999) and microbial interactions (Miconnet et al., 2005).

54 3.2 Materials and Methods

O2 between 20 and 100%

CO2 between 0 and 25%

O2

CO2 N2

(20, 25, 55)

(35.3, 18.75, 45.95)(40, 0, 60)

(53.75, 12.5, 33.75)

(75.3, 6.25, 18.45)

(61.25, 25, 13.75)

Figure 3.4: Gas conditions of the in vitro validation experiments for P. fluorescens

represented as circles (©) in the gas mixture triangle. The corresponding gas concen-

trations are given between brackets in the following order: (% O2, % CO2, % N2).

The tested gas mixtures for identification of the model are marked with crosses (×).

The following sections explain how the primary and overall error of the pre-

dictive growth models for Pseudomonas fluorescens and Listeria innocua were

quantified.

3.2.6.1 Estimation of primary errors

For primary validation of the P. fluorescens overall models, additional in vitro

experiments were carried out at six gas conditions in the region of interest with

an inoculum density around 3.5 log cfu cm−2. The tested gas conditions were

(% O2, % CO2, % N2) : (40, 0, 60) ; (75.3, 6.25, 18.45) ; (61.25, 25, 13.75) ;

(53.75, 12.5, 33.75) ; (35.3, 18.75, 45.95) ; (20, 25, 55). The gas conditions are

represented schematically in Figure 3.4. To quantify the primary error of the L.

innocua overall model, two additional sets of in vitro experiments were carried

out at gas conditions in the region of interest. The first set of 5 validation

experiments was done with an inoculum density of 3.5 log cfu cm−2. The tested

gas conditions were (% O2, % CO2, % N2): (87.5, 12.5, 0) ; (75, 25, 0) ; (53.75,

12.5, 33.75) ; (47.5, 25, 27.5) ; (20, 25, 55). The second set consisted of 9

validation experiments, where the inoculum density was 4.6 log cfu cm−2. The

gas conditions were identical to those used for model identification.

The experimental method was the same as explained previously for the in

vitro experiments that were used for model calibration. The validation of the

Analysis and modelling of high oxygen effects on microbial growth 55

overall model was quantified by determining, firstly, the adjusted R2, the RMSE

(in order to use the same measures as during model identification) and, secondly,

the bias and accuracy factors. The latter were calculated in two different ways.

First, they were calculated using the natural logarithm of µmax as described by

Baranyi et al. (1999) and shown in equation (3.11) (accuracy factor Aµ) and

equation (3.12) (bias factor Bµ).

Aµ = exp

√∑ki=1(ln(µmax,overall(xi))− ln(µmax,primary(xi)))2

k

(3.11)

Bµ = exp

[∑ki=1(ln(µmax,overall(xi))− ln(µmax,primary(xi)))

k

](3.12)

k is the number of validation gas conditions, µmax,overall(xi) is the maximum

specific growth rate at the ith specific validation gas condition (xi) predicted

with the overall model and µmax,primary(xi) is the maximum specific growth rate

at the ith specific gas condition (xi) resulting from the fit of the primary model

to the validation data.

Secondly, the bias and accuracy factors were calculated on all count data (not

averages) as shown in equations (3.13) and (3.14). The approach to calculating

the bias and accuracy factors on count data is new and introduced for the first

time. We consider it as a more valid approach for quantitative validation of

one-step regression models.

Acount = exp

√√√√∑k

i=1

∑lij=1(noverall(xi, tij)− ncount

ij )2∑ki=1 li

(3.13)

Bcount = exp

[∑ki=1

∑lij=1(noverall(xi, tij)− ncount

ij )∑ki=1 li

](3.14)

k is the number of validation gas conditions, li is the number of measure-

ment points of the ith validation gas condition, noverall(xi, tij) is the bacterial

concentration as base 10 logarithm at a specific gas condition (xi) and time

(tij) predicted with the overall model (using the actual inoculum density in the

validation experiments as the value for n0) and ncountij is the observed bacterial

count as base 10 logarithm at the ith specific gas condition (xi) and time (tij).

The per cent discrepancy (%D) and per cent bias (%B) are calculated as de-

scribed in Baranyi et al. (1999) as shown in equation (3.15) and equation (3.16),

respectively.

56 3.2 Materials and Methods

%D = (A− 1)100% (3.15)

%B = sgn(lnB)(exp |lnB| − 1)100% (3.16)

sgn(lnB)= +1 if lnB > 0, denoting that the model predicts faster growth

than the observations (fail safe)

= 0 if lnB = 0 , denoting a perfect prediction of the data

= −1 if lnB < 0, denoting that the model predicts slower growth

than the observations (fail dangerous).

Percentages discrepancy and bias calculated using Acount and Bcount will be

denoted by %Dcount and %Bcount. %D and %B calculated based on Aµ and Bµ

will be denoted by %Dµ and %Bµ.

Additionally, the 95% prediction limits of the overall model were estimated

for each validation gas condition using a Monte Carlo procedure written in

Matlab® 6 (Rubinstein, 1981). To generate samples of a vector of corre-

lated Gaussian random parameters, a Cholesky decomposition of the variance-

covariance matrix of the fitted parameters was carried out. Multiplying the

Cholesky decomposition matrix with a vector from which each element is ran-

domly chosen from a standard normal distribution yields a vector of deviation.

Summing the deviation vector with the mean estimated parameter vector yields

a new parameter vector. This was repeated 5000 times yielding a parameter vec-

tor sample with the same covariance structure as the fitted parameter values.

The model equation was calculated in 100 time points per inserted parameter set

resulting in a 5000x100 matrix for each of the validation gas conditions. At this

stage also the variation of the measurements was included. The mean squared

error of the model fit on the calibration data set was taken as a measure for the

variation on in vitro measurements. To include this variation, 5000x100 mea-

surement errors were randomly sampled from a normal distribution with mean

zero and a variance equal to the root mean squared error of the model fit and

added. Per time point, the calculated values were sorted from small to large.

The lower and higher 95% prediction limit, respectively, of each time point was

determined by the 126th and 4875th element of the sorted row. The interval

as we calculated it here is the 95% confidence interval for a new prediction or

briefly the 95% prediction interval, because both the confidence limits for the

parameters and the variation of the measurements were included.

Analysis and modelling of high oxygen effects on microbial growth 57

3.2.6.2 Estimation of overall errors

The results of the experiments on fresh-cut butterhead lettuce were used to

calculate the overall errors of the predictive growth models. Similarly as for

primary validation, overall validation was quantified with the adjusted R2 and

RMSE. Also, the bias and accuracy factors calculated on all count data were

determined as shown in equation (3.13) and (3.14). Percentages discrepancy

and bias were calculated according to equation (3.15) and (3.16). The 95%

prediction intervals were calculated as explained in section 3.2.6.1.

Of special interest when validating the growth of pathogens on real foods,

is the competition with the naturally occurring spoilage organisms (Micon-

net et al., 2005). Several authors (e.g. Cornu (2001) and Gimenez and Dal-

gaard (2004)) described a deceleration of the growth of the minority population

(pathogen) when the majority population (spoilage organisms) reaches its max-

imum population density. This effect has been called after Jameson (1962) who

first described this effect for intestinal organisms as follows: ‘When two intesti-

nal organisms are inoculated together into a liquid medium, each organism nor-

mally follows at first a growth pattern similar to that which would have followed

from a similar inoculum in the same medium in the absence of a competitor.

Neither organism normally exhibits its awareness, to any appreciable degree, of

the other’s presence, until the bacterial density of one or other organism has

risen to the maximum population density, when both organisms end their rapid

multiplication.’ In our case, Pseudomonaceae were considered to be the ma-

jor group of micro-organisms contaminating lettuce. To take into account the

Jameson effect when validating the growth of L. innocua on fresh-cut lettuce,

the times to reach the maximum population density of Pseudomonaceae were

calculated per gas condition using the P. fluorescens growth model. Growth

of L. innocua was considered to follow the developed L. innocua growth model

until pseudomonads reached their maximum level. Afterwards, growth of L.

innocua was considered to be zero and population density was kept at the same

level from this time on. Validation parameters were calculated as described

above. The calculation of a 95% prediction interval was not possible in this

case.

58 3.3 Results

3.3 Results

3.3.1 Primary growth model

Growth curves for Pseudomonas fluorescens and Listeria innocua were obtained

for all nine gas combinations (Figure 3.5). Oxygen and carbon dioxide concen-

tration did not significantly influence the population density of Pseudomonas

fluorescens in the stationary phase (nmax), which was approximately 8.5 log

cfu cm−2 in all gas conditions. The maximum population density of Listeria

innocua was between 6.2 and 7.4 log cfu cm−2. Although in the gas conditions

with 0% CO2 (Figure 3.5a at the right) there seems to be an influence of the

gas atmosphere on the maximum population density of L. innocua, this trend

was not statistically significant.

For P. fluorescens the time to reach nmax was influenced by both oxygen

and carbon dioxide concentration, while for L. innocua only carbon dioxide

had an influence. In absence of CO2 (Figure 3.5a, left figure), the maximum

population density of P. fluorescens was reached after 110 hours (when 20%

oxygen was applied) to 200 hours (when 100% O2 was applied). P. fluorescens

bacteria stored under a gas atmosphere with 12.5% CO2 (Figure 3.5b, left figure)

reached the maximum population density after 180 h (when combined with 20%

of oxygen) and after 300 h (in combination with 87.5% oxygen). At atmospheres

with 25% CO2 (Figure 3.5c, left figure) the time to reach nmax was even 350

and 600 h in combination with 20% O2 and 75% O2 respectively.

In the absence of CO2, nmax of Listeria innocua was reached after approx-

imately 150 hours (Figure 3.5a, right figure). Bacteria stored under a gas at-

mosphere with 12.5% CO2 reached the maximum population density after ap-

proximately 200 h (Figure 3.5b, right figure). At atmospheres with 25% CO2

the bacteria needed 300 h up to 600 h to reach nmax (Figure 3.5c, right figure).

The observed delays in reaching nmax at high oxygen and/or elevated carbon

dioxide concentrations was due to a prolongation of the lag time combined with

a decreased maximum specific growth rate. Lag time (λ), maximum specific

growth rate (µmax), maximum population density (nmax) and their correspond-

ing 95% confidence limits were estimated using the Baranyi-equation and are

listed together with the values for MSE in Table 3.2 and 3.3.

Analysis and modelling of high oxygen effects on microbial growth 59

4

5

6

7

8

9(a)

4

5

6

7

8

9

Log

cfu

cm−

2

(b)

0 100 200 300 400 500 600 700

4

5

6

7

8

9

Time (hours)

(c)

4

5

6

7

8

9(a)

4

5

6

7

8

9

Log

cfu

cm−

2

(b)

0 100 200 300 400 500 600 700

4

5

6

7

8

9

Time (hours)

(c)

Figure 3.5: In vitro growth on nutrient agar of Pseudomonas fluorescens (left) and

Listeria innocua (right) at 7 ◦C under atmospheres with 0% CO2 (a), 12.5% CO2 (b)

and 25% CO2 (c). For figure a, b and c, � and dash-dotted lines denote average

bacteria counts and fitted Baranyi curves at oxygen concentrations of 100%, 87.5%

and 75% respectively; × and dashed lines denote average bacteria counts and Baranyi

curves at 60%, 53.75% and 47.5% O2, respectively. Average counts and fitted curves

at 20% oxygen are marked with N and solid lines.

60 3.3 Results

Table 3.2: Estimated parameters and corresponding approximate 95% confidence

limits of lag time (λ), maximum specific growth rate (µmax) and maximum population

density (nmax) for the growth of Pseudomonas fluorescens at 7 ◦C under different high

oxygen atmospheres, according to the Baranyi model (Baranyi and Roberts, 1994).

Oxygen Carbon dioxide λ µmax nmax MSE

(%) (%) (h) (h−1) (log cfu cm−2)

20 0 16 ± 9 0.123 ± 0.015 8.58 ± 0.10 0.053

60 0 17 ± 7 0.113 ± 0.011 8.75 ± 0.08 0.034

100 0 4 ± 9 0.075 ± 0.006 8.89 ± 0.12 0.038

20 12.5 28 ± 9 0.079 ± 0.006 8.30 ± 0.11 0.033

53.75 12.5 29 ± 17 0.074 ± 0.009 8.34 ± 0.22 0.111

87.5 12.5 108 ± 28 0.057 ± 0.012 8.29 ± 0.18 0.106

20 25 67 ± 21 0.044 ± 0.005 8.19 ± 0.12 0.054

47.5 25 55 ± 20 0.038 ± 0.003 8.32 ± 0.13 0.039

75 25 98 ± 32 0.025 ± 0.003 8.24 ± 0.21 0.051

Table 3.3: Estimated parameters and corresponding approximate 95% confidence

limits of lag time (λ), maximum specific growth rate (µmax) and maximum population

density (nmax) for the growth of Listeria innocua at 7 ◦C under different high oxygen

atmospheres, according to the Baranyi model (Baranyi and Roberts, 1994).

Oxygen Carbon dioxide λ µmax nmax MSE

(%) (%) (h) (h−1) (log cfu cm−2)

20 0 0 ± 8 0.063 ± 0.005 7.43 ± 0.06 0.011

60 0 2 ± 29 0.055 ± 0.019 6.22 ± 0.17 0.090

100 0 0 ± 8 0.066 ± 0.006 6.79 ± 0.06 0.011

20 12.5 57 ± 18 0.057 ± 0.010 6.99 ± 0.41 0.060

53.75 12.5 74 ± 21 0.056 ± 0.012 6.50 ± 0.09 0.052

87.5 12.5 12 ± 25 0.034 ± 0.004 6.61 ± 0.07 0.022

20 25 61 ± 58 0.025 ± 0.007 6.35 ± 0.14 0.093

47.5 25 67 ± 46 0.019 ± 0.003 6.69 ± 0.16 0.054

75 25 56 ± 58 0.018 ± 0.003 6.39 ± 0.16 0.058

Analysis and modelling of high oxygen effects on microbial growth 61

0.05 0.1 0.15 0.2 0.250

0.05

0.1

0.15

0.2

0.25

λ−1

µ max

Figure 3.6: Plot of estimated µmax against estimated λ−1 (including 95% confidence

limits) for the nine tested gas conditions. The presented line has a slope equal to 2.49,

the initial value of parameter lm for overall model #2. The outlier represents the gas

condition with 100% oxygen.

3.3.2 Secondary growth models

3.3.2.1 Pseudomonas fluorescens

The parameter estimates of the secondary models are represented in Table 3.4.

Model parameters were considered significant when their approximate 95% con-

fidence interval did not include zero. As can be seen from the table, parameters

lO2 , mO2 and mCO2 were not significant. However, the secondary model esti-

mates were only used as starting values for the one-step regression procedure

for overall model identification. Further conclusions on the gas-effect were only

drawn after overall modelling (see Section 3.3.3). Parameter lm (equation (3.6))

is defined as the product of µmax and λ and was initialised at 2.49, which is the

product of µmax and λ averaged over all gas conditions. In Figure 3.6 the rela-

tion between µmax and λ−1 is shown. The presented line has a slope equal to

2.49, the value at which parameter lm is initialised in the one-step regression

procedure. From this graph it can be seen that the linear relationship is accept-

able for all gas condition except one (namely 100% O2). However, considering

the large confidence limits of the estimates for λ−1, puts this ‘outlier’ in per-

spective. In any case, the slope value will be only used as a starting value in

the one-step regression procedure.

3.3.2.2 Listeria innocua

Carbon dioxide was found to have a significant effect on both growth parameters,

whereas oxygen concentrations did not. Secondary models to describe the effect

62 3.3 Results

of carbon dioxide on µmax and λ were used (respectively equation (3.7) and

(3.8)). Secondary growth model parameters and corresponding 95% confidence

intervals are given in Table 3.5. The adjusted R2 and MSE for the lag time-

model were 0.87 and 310.9 respectively. For the µmax-model, the goodness-of-fit

parameters equalled 0.78 (R2adj) and 0.0001 (MSE).

3.3.3 Overall growth models

Looking at Figure 3.5, it is apparent that the variation on the experimental data

seems to be dependent on the gas condition applied (as also quantified by the

MSE values reported in Table 3.2 and 3.3). This was confirmed by means of the

Levene’s test, proving that the variances were non-homogeneous. Consequently

a weighted least squares optimisation procedure was used to estimate the overall

growth models.

3.3.3.1 Pseudomonas fluorescens

The growth parameters n0 and nmax were considered independent of the oxygen

and carbon dioxide concentration and were initialised at 3.5 and 8.5 log cfu cm−2

respectively. Model parameters were considered significant when their approx-

imate 95% confidence interval did not include zero. All the model parameters

were significant, for both models (Table 3.4). The adjusted R2 was equal for

both models (0.97). The RMSE was 0.34 and 0.36 for model #1 and model #2,

respectively. Correlation between the model parameters was low. The residuals

were distributed around zero and the normal probability plots did not show any

abnormalities (data not shown).

Assuming that the inoculum density is 3 log cfu cm−2 and that a population

density of 7 log cfu cm−2 is the critical contamination limit of P. fluorescens

before visible spoilage of fresh-cut vegetables occurs, the time needed to reach

this population density in our model conditions is visualised in Figure 3.7 for

model #1. At constant CO2 concentration, the time to reach this limit in-

creased with increasing oxygen concentration. E.g., it took 200, 250 or 300

hours to reach a population density of 7 log cfu cm−2 when a carbon dioxide

concentration of 20% was combined with respectively 33%, 59% or 72% O2. At

constant oxygen concentrations the effect of an increasing CO2-concentration is

more pronounced at high carbon dioxide concentrations compared to low carbon

dioxide concentrations. For example, applying 8% instead of 2% carbon dioxide

at a concentration of 70% oxygen increases the time to reach 7 log cfu cm−2

from 100 to 150 hours. At carbon dioxide concentrations near 25%, the same

Analysis and modelling of high oxygen effects on microbial growth 63

Table 3.4: Parameter estimates and corresponding approximate 95% confidence in-

tervals for the secondary models and overall model #1 and #2 for Pseudomonas

fluorescens.

Parameter Secondary model Overall model #1 Overall model #2

n0 3.54±0.02 3.57±0.02

(log cfu cm−2)

nmax 8.55±0.01 8.54±0.01

(log cfu cm−2)

l 2.5±1.2 3.23±0.06

(ln(h))

lO2 0.0009±0.0163 -0.016±0.002

(ln(h) %−1)

lCO2 0.08±0.05 0.051±0.003

(ln(h) %−1)

lm 1.6±0.1

(-)

m 0.38±0.02 0.395±0.003 0.369±0.002

(h−1/2)

mO2 -0.0009±0.0301 -0.00133±0.00003 -0.00087±0.00002

(h−1/2 %−1)

mCO2 -0.006±0.090 -0.00641±0.00008 -0.00631±0.00006

(h−1/2 %−1)

increase of 50 hours is accomplished when increasing the CO2 concentration

from 20% to 22%.

3.3.3.2 Listeria innocua

The growth parameters n0 and nmax were considered independent of the oxygen

and carbon dioxide concentration and were initialised at 3.5 and 6.5 log cfu

cm−2, respectively. For the other parameters, the obtained parameter estimates

from secondary modelling were used to initialise the non-linear least squares

search procedure for the overall model. In Table 3.5, the parameter estimates

for the overall model are given. The adjusted R2 was 0.92. The RMSE was

0.34. The residuals were distributed around zero and the normal probability

plots did not show any abnormalities (data not shown).

64 3.3 Results

100

100150

150

150200

200

200

250

250

250

300

300350

400

CO2 (%)

O2 (

%)

0 5 10 15 20 2520

30

40

50

60

70

80

90

100

Figure 3.7: Contour plot representing the time (in h) needed to reach a population

density of 7 log cfu cm2 (with n0 = 3 log cfu cm−2) as a function of oxygen and carbon

dioxide concentration, calculated from overall model #1 for Pseudomonas fluorescens.

Table 3.5: Parameter estimates and corresponding approximate 95% confidence in-

tervals for the secondary and overall models for Listeria innocua.

Parameter Secondary model estimates Overall model estimates

n0 (log cfu cm−2) 3.69±0.02

nmax (log cfu cm−2) 6.85±0.01

l (h2 %−1) 160±99 123±11

m (h−1 %−1) 0.0016±0.0008 0.00204±0.00005

CO2max (%) 39±14 31.5±0.2

Analysis and modelling of high oxygen effects on microbial growth 65

0 5 10 15 20 2520

30

40

50

60

70

80

90

100

Carbon dioxide (%)

Oxy

gen

(%)

Figure 3.8: Risk area plot for L. innocua when assuming an initial contamination

level of Pseudomonaceae of 5 and an initial contamination level of L. innocua of 1

cfu g−1. At combinations of O2 and CO2 situated in the grey region, L. innocua

levels were higher than 100 cfu g−1 at the end of the shelf life which was defined by a

concentration of 8 log cfu g−1 of Pseudomonaceae.

3.3.4 Calculation of risk areas

The overall growth model for L. innocua was combined with overall growth

model #1 for P. fluorescens to calculate a ‘risk area’. ‘Risk areas’ were defined

by Devlieghere et al. (2001) as combinations of factors at which food pathogens

can develop to an unacceptable level before spoilage occurs. As a theoretical

example, a risk area was calculated for fresh-cut lettuce. According to Ragaert

(2005) and Debevere et al. (2006), it was assumed that the initial contamination

level for Pseudomonaceae is 105 cfu g−1 and decay related with spoilage occurs

at a concentration of 108 cfu g−1. The initial contamination level of L. innocua

was assumed to be 1 cfu g−1 and was allowed to reach 102 cfu g−1 at the end of

the shelf life as proposed by several authors (e.g. Nguyen-the and Carlin (1994);

Chen et al. (2003) and according to guideline SANCO/4198/2001 Rev. 15 of the

Commission of the European Communities (for Listeria monocytogenes)). The

resulting ‘risk area’ plot is shown in Figure 3.8. The combinations of oxygen

and carbon dioxide that imply a potential health risk are represented in grey.

3.3.5 Model validation

3.3.5.1 Estimation of primary errors

The primary errors of the overall models were estimated using the results of the

in vitro validation experiments. The actual inoculum density in the validation

experiments was taken as value for n0. The validation parameters adjusted

R2 (R2adj), mean squared error (MSE), bias and accuracy factors are given in

66 3.3 Results

Table 3.6: Primary validation parameters of the overall models for growth of Pseu-

domonas fluorescens and Listeria innocua.

Validation parameter Model #1 Model #2 Model

P. fluorescens P. fluorescens L. innocua

R2adj 0.94 0.91 0.81

RMSE 0.52 0.65 0.52

Bcount (% Bcount) 0.78 (-28%) 0.70 (-42%) 0.98 (-2%)

Acount (% Dcount) 1.66 (66%) 1.89 (89%) 1.69 (69%)

Bµ (% Bµ) 0.89 (-12%) 0.89 (-12%) 0.70 (-43%)

Aµ (% Dµ) 1.29 (29%) 1.29 (29%) 1.98 (98%)

Table 3.6. The MSE-value was smaller for Pseudomonas fluorescens model

#1 than for model #2, meaning a better correspondence between measured

and predicted values for the former model. The bias factor was in both cases

smaller than one, which means that both models underestimated the growth.

Also the overall model for Listeria innocua underestimated the growth but to

a lesser extent (% Bcount= -2%).

However, even if data points do not perfectly match the model prediction

(which has to be interpreted as the average response of the bacterial cells), they

can still be situated in the 95% prediction region of the model. The upper

and lower 95% prediction boundaries and measured values for the in vitro val-

idation experiments of Pseudomonas fluorescens are represented in Figure 3.9,

respectively. On a total amount of 111 validation points, 18 points fall outside

the 95% prediction region, being 16%. From the 18 validation points that fall

outside the 95% prediction region, 16 points are situated in the fail dangerous

region. The upper and lower 95% prediction boundaries and measured values

for the two validation experiments of Listeria innocua are represented in Figure

3.10 and 3.11, respectively. Of a total of 382 validation points, 51 points (13%)

fall outside the 95% prediction region. Note that in the case of the L. innocua

validation experiments the value for n0 was taken somewhat lower (3.5 log cfu

cm−2) than the inoculum density (4.0 log cfu cm−2) which was counted from

the inoculum suspension on day 0. This decision was made since the actual

count was unexpectedly high (the bacteria were grown, diluted and inoculated

in such a way to reach approximately 3.5 log cfu cm−2) which could also be

related to the microbial counts after 22 hours at all gas conditions (average 3.6

log cfu cm−2). It was concluded that the count of the actual inoculum density

was not accurate.

Analysis and modelling of high oxygen effects on microbial growth 67

3

5

7

9

40% O2 + 0% CO

275.3% O

2 + 6.25% CO

2

3

5

7

9

53.75% O2 + 12.5% CO

2

Log

cfu.

cm−

2

35.3% O2 + 18.75% CO

2

0 100 200 300 400 500 600

3

5

7

9

20% O2 + 25% CO

2

Time (h)0 100 200 300 400 500 600

61.25% O2 + 25% CO

2

Time (h)

Figure 3.9: Results of the in vitro validation experiments on P. fluorescens shown

per gas condition (circles). The full line represents the average model prediction. The

dashed lines represent estimated lower and upper 95% prediction limits for model #1

as calculated using a Monte Carlo simulation.

68 3.3 Results

3

5

7

9

53.75% O2 + 12.5% CO

287.5% O

2 + 12.5% CO

2

3

5

7

9

20% O2 + 25% CO

2

Log

cfu.

cm−

2

0 100 200 300 400 500 600

47.5% O2 + 25% CO

2

Time (h)

0 100 200 300 400 500 600

3

5

7

9

75% O2 + 25% CO

2

Time (h)

Figure 3.10: Results of the first set of in vitro validation experiments on L. innocua

shown per gas condition (circles). The inoculum density was 3.5 log cfu cm−2. The

full line represents the average model prediction. The dashed lines represent estimated

lower and upper 95% prediction limits for the overall model as calculated using a Monte

Carlo simulation.

Analysis and modelling of high oxygen effects on microbial growth 69

3

5

7

9

20% O2 + 0% CO

220% O

2 + 12.5% CO

220% O

2 + 25% CO

2

3

5

7

9

60% O2 + 0% CO

2

Log

cfu.

cm−

2

53.75% O2 + 12.5% CO

247.5% O

2 + 25% CO

2

Time(hours)

0 200 400 600

3

5

7

9

100% O2 + 0% CO

2

0 200 400 600

87.5% O2 + 12.5% CO

2

Time (h)0 200 400 600

75% O2 + 25% CO

2

Figure 3.11: Results of the second set of in vitro validation experiments on L. innocua

shown per gas condition (circles). The inoculum density was 4.6 log cfu cm−2. The

full line represents the average model prediction. The dashed lines represent estimated

lower and upper 95% prediction limits for the overall model as calculated using a Monte

Carlo simulation.

70 3.3 Results

3.3.5.2 Estimation of overall errors

The growth of GFP tagged P. fluorescens on fresh-cut lettuce is given in Figure

3.12. The inoculum density of the GFP tagged P. fluorescens was taken equal

to 3.9 log cfu g−1, which was the average initial count of 10 samples (2 samples

at each of the five different gas conditions). The fresh-cut lettuce was naturally

contaminated with 5.0 log cfu g−1 Pseudomonaceae at the start of the experi-

ment. Figure 3.13 shows the growth of naturally present Pseudomonas species

and the total amount of Pseudomonas species (the latter being the sum of the

inoculated GFP tagged strain and the naturally present Pseudomonaceae). The

in vivo growth of the GFP tagged P. fluorescens as well as the naturally present

pseudomonads are clearly retarded by both high O2 (75%) and CO2 (15%) levels

as was the case in in vitro circumstances. From Figure 3.12 and 3.13 it is clear

that the observed growth on fresh-cut lettuce was in good agreement with the

model predictions. The overall validation parameters of the P. fluorescens are

given in Table 3.7. The growth at the gas condition with 5% O2 was represented

in Figure 3.12 and 3.13 but was also considered in the validation, although the

predictive model was originally built for oxygen concentrations between 20 and

100%. The validation parameters calculated with or without inclusion of data

measured at 5% O2 are given. Also the 95% prediction intervals were calculated

for this experiment. For the analysis of the GFP tagged P. fluorescens strain

only 6 of 56 validation data points (=11%) are situated outside the 95% pre-

diction intervals. For the naturally present Pseudomonas species this was 30%

(17 of 56 points). When the gas condition with 5% O2 was also considered, 9 of

70 validation points (=13%) for the GFP tagged P. fluorescens strain and 19

of 70 validation points (=27%) for the naturally present Pseudomonas species

were situated outside the 95% prediction intervals.

The evolution of the total aerobic mesophilic count of the lettuce is given in

Figure 3.14. From the counts represented in Figure 3.13 (Pseudomonads) and

3.14 (AMC) it is obvious that pseudomonads represented a large part of the

aerobic mesophilic bacteria. Consequently, the effect of the gas conditions on the

AMC can be partly explained by the gas effect on the pseudomonads. Indeed,

growth is faster at gas conditions without CO2 (see Figure 3.14). Oxygen effects

are less clear.

Since the results of the two sets of experiments on L. innocua did not differ

significantly, the data were pooled for further analysis. The average inoculum

density (3.8 log cfu g−1) was taken as value for n0. The growth of L. innocua

on fresh-cut butterhead lettuce is represented together with the average model

predictions and 95% prediction intervals in Figure 3.15. Although the model was

Analysis and modelling of high oxygen effects on microbial growth 71

5% O2 + 15% CO

2

3

5

7

9

20% O2 + 0% CO

2

Log

cfu.

g−1

20% O2 + 15% CO

2

0 100 200 300

3

5

7

9

75% O2 + 0% CO

2

Time (h)100 200 300

75% O2 + 15% CO

2

Time (h)

Figure 3.12: Results of the in vivo validation experiments with the GFP tagged P.

fluorescens mutant shown per gas condition (circles). The inoculum density was 3.9

log cfu g−1. The full line represents the average model prediction. The dashed lines

represent estimated lower and upper 95% prediction limits for the overall model as

calculated using a Monte Carlo simulation.

Table 3.7: Overall validation parameters of the overall model #1 for growth of GFP

tagged P. fluorescens, naturally present pseudomonads and total Pseudomonas (sum

of inoculated GFP tagged P. fluorescens and initially present pseudomonads) with (+)

or without (-) inclusion of gas condition 5% O2 + 15% CO2.

Validation GFP tagged Naturally present Total pseudomonads

parameter P. fluorescens pseudomonads inoculated

+ naturally present

+ - + - + -

R2adj 0.88 0.88 0.68 0.74 0.85 0.85

RMSE 0.55 0.56 0.67 0.62 0.51 0.49

Bcount 1.31 1.33 1.50 1.42 1.35 1.37

(% Bcount) (31%) (32%) (50%) (42%) (35%) (37%)

Acount 1.68 1.70 1.89 1.81 1.61 1.61

(% Dcount) (68%) (70%) (89%) (81%) (61%) (61%)

72 3.3 Results

5% O2 + 15% CO

2

3

5

7

9

20% O2 + 0% CO

2

Log

cfu

g−1

20% O2 + 15% CO

2

0 100 200 300

3

5

7

9

75% O2 + 0% CO

2

Time (h)100 200 300

75% O2 + 15% CO

2

Time (h)

Figure 3.13: Results of the in vivo validation experiments on Pseudomonas species.

The naturally present Pseudomonas species are represented by � and the total Pseu-

domonas species (inoculated plus naturally present) are given by stars. The initial

density was 5.0 log cfu g−1. The full line represents the average model prediction.

The dashed lines represent estimated lower and upper 95% prediction limits for the

overall model as calculated using a Monte Carlo simulation.

Analysis and modelling of high oxygen effects on microbial growth 73

0 100 200 300

4

5

6

7

8

9

10

Time (hours)

Log

cfu.

g−1

Figure 3.14: Changes in the population of aerobic mesophilic bacteria on fresh-cut

butterhead lettuce inoculated with GFP tagged mutant P. fluorescens. The symbols

represent average values of 2 replicates, bars represent 95% confidence intervals. Dif-

ferent symbols denote growth at different gas conditions (4 : 20% O2 + 0% CO2; �

: 75% O2 + 15% CO2; ◦ : 75% O2 + 0% CO2; ♦ : 5% O2 + 0% CO2; × : 5% O2 +

15% CO2).

constructed for oxygen concentrations between 20 and 100%, also the growth

of L. innocua on cut lettuce stored at 5% O2 is represented with the model

prediction and taken into account for validation of the model. This is justified

by the fact that the in vitro growth of L. innocua was only influenced by CO2

and not by high O2. Also in low O2 MAP, the growth of Listeria is influenced

by CO2 and not by O2 (Bennik et al., 1995).

As can be seen from Figure 3.15, the observed growth on fresh-cut lettuce is

not following the predictions well, especially at the conditions without CO2. For

these conditions 56% of the validation points is situated outside the 95% pre-

diction interval. For the conditions with 15% CO2, 20% of the validation points

fall outside the 95% prediction interval. Also the high values for %Bcount (98%)

and %Dcount (234%) indicate the large discepancy between observed growth and

model predictions. Most of the points are situated in the fail safe region (pos-

itive %Bcount value) representing a slower growth of the bacteria on fresh-cut

lettuce than predicted from the in vitro results.

Since on fresh-cut lettuce a slower growth of L. innocua compared to in

vitro (monoculture) circumstances occured, the hypothesis of interactions with

other micro-organisms was tested in a next validation step. The hypothesis was

based on the Jameson effect (as explained in Section 3.2.6.2). The predominant

group of pseudomonads were considered as the majority population on fresh-cut

74 3.3 Results

3

5

7

9

5% O2 + 0% CO

25% O

2 + 15% CO

2

3

5

7

9

20% O2 + 0% CO

2

Log

cfu.

g−1

20% O2 + 15% CO

2

Time(hours)

0 100 200 300

3

5

7

9

75% O2 + 0% CO

2

Time (h)100 200 300

75% O2 + 15% CO

2

Time (h)

Figure 3.15: Results of the in vivo validation experiments on L. innocua shown

per gas condition (circles). The inoculum density was 3.8 log cfu g−1. The full line

represents the average model prediction. The dashed lines represent estimated lower

and upper 95% prediction limits for the overall model as calculated using a Monte

Carlo simulation.

Analysis and modelling of high oxygen effects on microbial growth 75

lettuce, influencing the growth of L. innocua when their maximum population

density is reached. To calculate the time to reach the maximum population

density of Pseudomonaceae, an inoculum density of 5.6 log cfu g−1 was chosen.

This value was based on the initial aerobic mesophilic count (see also Figure 3.17

(a)) on the lettuce used for the in vivo experiment of L. innocua, as the group

of aerobic mesophilic bacteria largely consists of pseudomonads as stated earlier

in this section (see also Figure 3.13 and 3.14). At the time the Pseudomonaceae

reached their maximum population density, the growth of L. innocua was con-

sidered to stop. Gas conditions with 5% O2 were omitted in this validation

step, since this O2 concentration falls outside the application range of the P.

fluorescens model. The resulting model prediction as shown in Figure 3.16 is in

better correspondence with the actual growth of L. innocua as compared to the

model prediction that does not take into account the interaction with the pseu-

domonads group (see Figure 3.15), especially at gas conditions without CO2.

This is also reflected in a lower %Dcount (103%). The low %Bcount (21%) is the

result of an overestimation of the growth at conditions without CO2 on the one

hand and an underestimation at conditions with 15% CO2 on the other hand.

34567

20% O2 + 0% CO

2Log

cfu

g−1

20% O2 + 15% CO

2

Time(hours)

0 100 200 300

34567

75% O2 + 0% CO

2

Time(hours)0 100 200 300

75% O2 + 15% CO

2

Time(hours)

Figure 3.16: Results of the in vivo validation experiments on L. innocua shown per

gas condition (circles) represented together with the average predicted growth with

incorporation of the Jameson effect.

The growth of aerobic mesophilic bacteria and lactic acid bacteria on the

fresh-cut lettuce inoculated with L. innocua was followed. The results are given

in Figure 3.17 (a) and (b), respectively. Aerobic mesophilic counts increased

during the storage from 5.6 log cfu g−1 at day 0 to 8.3 - 9.2 log cfu g−1 at day 10

76 3.3 Results

0 100 200 300

4

5

6

7

8

9

10

Time (hours)

Log

cfu

g−1

(a)

0 100 200 300

4

5

6

7

8

9

10

Time (hours)

Log

cfu

g−1

(b)

Figure 3.17: Changes in the population of aerobic mesophilic bacteria (a) and lactic

acid bacteria (b) on fresh-cut butterhead lettuce inoculated with L. innocua. The

symbols represent average values of 3 replicates, bars represent 95% confidence inter-

vals. Different symbols denote growth at different gas conditions (4 : 20% O2 + 0%

CO2; � : 75% O2 + 15% CO2; ◦ : 75% O2 + 0% CO2; ♦ : 5% O2 + 0% CO2; × :

5% O2 + 15% CO2).

(240 h). These bacteria grew fastest at the conditions without CO2 and 5 (‘♦’)

or 20% O2 (‘4’). Growth could be retarded by increasing the oxygen level to

75% (‘◦’) or by application of 5% O2 + 15% CO2 (‘×’), both conditions having a

similar effect. Extra growth inhibition was achieved when 75% O2 was combined

with 15% CO2 (‘�’). The differences between gas conditions were most obvious

between 1 (24 h) and 6 (144 h) days and gradually diminished towards the end

of the experiment after 10 days, indicating that high O2 conditions mainly affect

the lag time and maximum growth rate and to a lesser extent the maximum

population density.

Before storage, lactic acid bacteria were present at a concentration of 4.7 log

cfu g−1 on the fresh-cut lettuce. This group of bacteria grew significantly during

storage until day 5 (120 h) in all treatments. At the end of the storage time

(starting from day 5), the counts at conditions with 15% CO2 were significantly

higher than at those without CO2 based on analysis of variance. After 10 days

of storage, the average counts were 6.9 log cfu g−1 for the CO2 treatments and

5.5 log cfu g−1 for the treatments without CO2.

3.3.6 pH measurements

The pH of nutrient agar gradually decreased from 7.0 to 6.0 and 5.7 after 10 days

of storage at 12.5 and 25% CO2 respectively. The pH of cut lettuce homogenate

before storage was 6.2 and did not decrease but increased after 3 days of storage

with 0.4 units (to pH=6.6) when 12.5 or 25% CO2 was applied. When no CO2

Analysis and modelling of high oxygen effects on microbial growth 77

was applied, the pH of 6.6 was also reached but only between 4 and 7 days of

storage.

3.4 Discussion

3.4.1 High O2 and CO2 effects on in vitro growth

The data presented in this chapter show that both high oxygen concentrations

and elevated carbon dioxide concentrations had a retarding effect on the in

vitro growth of P. fluorescens at 7 ◦C. On the contrary, the in vitro growth of

L. innocua was only affected by carbon dioxide and not by superatmospheric

oxygen concentrations.

3.4.1.1 Pseudomonas fluorescens

Different authors previously reported on the sensitivity of Pseudomonas spp. to

carbon dioxide (Bennik et al., 1998; Enfors and Molin, 1980; Eyles et al., 1993;

Gill and Tan, 1979; Hendricks and Hotchkiss, 1997).

The most prominent and consistent effect of CO2 is a reduction of the maxi-

mum specific growth rate (µmax). The results presented on µmax by Bennik et al.

(1998) are in good agreement to those from our experiments for atmospheres

where different CO2 levels were combined with 20% O2.

Our experiments also showed that carbon dioxide lengthens the lag time.

Published studies on the effect of CO2 on lag times of P. fluorescens show in-

consistencies. Some studies report on a prolonging effect on lag time (Hendricks

and Hotchkiss, 1997), where in other studies no effect on lag time is observed

(Bennik et al., 1998; Eyles et al., 1993). As lag times are not only influenced

by the environmental conditions during the experiment, but also by the physi-

ological status of the cells at the start of the experiment, the inconsistencies in

lag time are probably caused by a different history of the cells, due to different

growth conditions of the inoculum (Swinnen et al., 2004).

The finding that the tested CO2 levels (0%, 12.5% and 25%) do not decrease

the maximum population density of P. fluorescens corresponds to the results of

Bennik et al. (1998) and Eyles et al. (1993). Bennik et al. (1998) only found a

reduction in maximum population density at concentrations of 50% CO2. It is

assumed that a decrease of maximum population density in low oxygen modified

atmospheres is caused by a limited availability of oxygen and not by the presence

of CO2 (Eyles et al., 1993).

78 3.4 Discussion

According to Amanatidou et al. (1999), CO2 levels combined with high oxy-

gen concentrations need not be higher than 20% to give consistent suppression

of microbial growth. From our study with P. fluorescens it was found that the

CO2 level can even be decreased to 12.5% when combined with high O2. Under

a gas atmosphere of 20% O2 plus 25% CO2, for example, the critical population

density of 107 cfu cm−2 was reached after 250 hours. The same magnitude of

growth retardation was obtained under 20% CO2 combined with 60% or more

oxygen, or under 14% CO2 combined with 86% O2. This is of particular inter-

est for the application of modified atmosphere packaging of fresh-cut vegetables,

since CO2 concentrations above 10 to 15% can cause deterioration, off-odours,

off-flavours or physiological disorders in several types of fresh-cut vegetables

(Gorny, 2001; Kader et al., 1989). Our study showed that high O2 and ele-

vated CO2 atmospheres resulted in both a prolongation of the lag time and a

reduction in maximum specific growth rate. The effect of high O2 alone on P.

fluorescens growth could be described as a reduction rather than a complete

inhibition. This is in accordance with results of Jacxsens et al. (2001) where the

time needed to reach more than 107 cfu g−1 of P. fluorescens was 173 hours at

70, 80 and 95% O2 compared to 127 hours at 5% O2. In our study, the max-

imum specific growth rates decreased with increasing O2 concentrations. The

retarding effect of high O2 levels combined with elevated CO2 concentrations,

was stronger than the effect of both gases separately corresponding to results

obtained by Amanatidou et al. (1999) and Van der Steen et al. (2003). Accord-

ing to Amanatidou et al. (1999), µmax at 80% O2 + 20% CO2 was only 75% and

34% of µmax-values at 0% O2 + 20% CO2 and 80% O2 + 0% CO2, respectively.

Van der Steen et al. (2003) found no effect of oxygen on the lag phase of P.

fluorescens at 4 ◦C, possibly due to differences in initial physiological status as

previously hypothesized. Also, the increase in maximum specific growth rate

(0.2 h−1 and 0.097 h−1 at 80% and 20% O2, respectively) and maximum popula-

tion density (10.4 and 9.9 log cfu cm−2 at 80% and 20% O2, respectively) found

by Amanatidou et al. (1999), was not confirmed in this study. In our study,

maximum population densities were the same for all tested gas atmospheres,

which was consistent with the findings of Jacxsens et al. (2001).

3.4.1.2 Listeria innocua

The in vitro growth of Listeria innocua was retarded by elevated CO2 levels but

not by high oxygen levels. These results were in agreement with the results of

Ogihara et al. (1993) and Day (2001) who found that in vitro growth of Listeria

monocytogenes was inhibited due to high levels of carbon dioxide and not of

Analysis and modelling of high oxygen effects on microbial growth 79

high oxygen levels in the applied gas mixtures. The effect of CO2 on Listeria

innocua was a prolongation of the lag time and a reduction of the maximum

specific growth rate.

Different authors previously examined the effect of carbon dioxide (in com-

bination with other factors as, e.g., pH and temperature) on Listeria monocyto-

genes in laboratory medium (Bennik et al., 1995; Farber et al., 1996; Fernandez

et al., 1997; Devlieghere et al., 2001). Bennik et al. (1995) found a decrease of

nmax of 0.68 log cfu cm−2 maximally at 50% CO2, whereas Farber et al. (1996)

and Fernandez et al. (1997) did not find an effect on nmax of carbon dioxide

concentrations up to 90% and 100%. This is in agreement with our results where

no effect of the gas atmosphere on the maximum population density was found.

Published studies on the effect of carbon dioxide on the lag time of Lis-

teria show inconsistencies. Farber et al. (1996) and Devlieghere et al. (2001)

reported a prolonged lag time when applying carbon dioxide. However, Bennik

et al. (1995) found no effect of CO2 on the lag time of Listeria monocytogenes.

As previously stated, these inconsistencies may have resulted from different

growth conditions of the inoculum. In our experiments, bacteria kept at gas at-

mospheres without carbon dioxide did not have a significant lag phase, whereas

under gas atmospheres with 25% CO2 lag times of approximately 60 h were

found.

The most consistent effect of carbon dioxide on Listeria is the effect on

the maximum specific growth rate. The maximum specific growth rate when

no carbon dioxide was applied was 0.06 h−1 and decreased to 0.02 h−1 with

application of 25% CO2. In comparison to earlier reported influence of CO2

on µmax of Listeria monocytogenes (Bennik et al., 1995; Farber et al., 1996;

Fernandez et al., 1997; Devlieghere et al., 2001), the effect on Listeria innocua

in our experiments was stronger. Whereas the addition of 25% CO2 resulted in

more than halving the µmax in our experiments, the same effect was only accom-

plished with 50% CO2 or more based on the results described in the literature.

This difference can be due to the fact that we used a relatively poor growth

medium in comparison to the other authors (Nutrient Agar instead of Brain

Heart Infusion Agar/Broth or Tryptic Soy Broth) or because of a difference

in response between Listeria innocua and L. monocytogenes. However, several

studies have demonstrated that the behaviour of both organisms is comparable

as affected by temperature, acidification and modified atmosphere (Hugas et al.,

1998; Thomas et al., 1999). Begot et al. (1997) examined differences among 58

strains of L. monocytogenes and 8 strains of Listeria innocua. Large variations

in lag times were found between the strains within one species, whereas the

80 3.4 Discussion

variations in generation times were less pronounced.

3.4.2 Modelling and primary model errors

Two overall models for P. fluorescens growth and one overall model for L.

innocua growth under high oxygen and elevated CO2 modified atmospheres

at 7 ◦C were built. To construct the model, a ’one-step regression’ approach

was followed in which the data were weighted according to the variances on the

error of the primary models. This approach yielded more precise estimates of the

parameters by using all growth data without applying unnecessary intermediate

steps as in two-step regression. Indeed, approximate 95% confidence intervals of

the lag phases resulting from primary modelling were rather large (see Table 3.2

and 3.3). Also, a high correlation between λ and µmax could be seen from the

correlation matrices (results not shown). These were indications that λ could

not be estimated well per experiment separately. In this case, the uncertainty

on the parameter estimates was expected to decrease when applying a one-step

regression procedure where all the information over the different gas conditions

was considered in one single optimisation procedure. From Table 3.5 it can

indeed be seen that the approximate 95% confidence intervals on the estimates of

the L. innocua overall model are largely reduced as compared to the secondary

model. Based on goodness-of-fit parameters, both models for Pseudomonas

fluorescens and the model for Listeria innocua had a good predictive quality.

Subsequently, in vitro validation experiments were carried out for the estima-

tion of primary model errors. Both overall models for Pseudomonas fluorescens

as well as the model for Listeria innocua slightly underestimated the in vitro

bacterial growth, the bacterial counts hence lying in the fail dangerous region.

This was reflected by a primary bias factor smaller than one. Based on its

higher adjusted R2-value, and lower MSE-value and primary accuracy factor,

model #1 for Pseudomonas fluorescens is preferable above model #2. A Monte

Carlo simulation was carried out in order to estimate the 95% prediction limits

of model #1 for Pseudomonas fluorescens and the model for Listeria innocua.

While one would expect 5% of the validation points to fall outside this prediction

region, we found a higher ratio of 16% and 13% (for Pseudomonas fluorescens

and Listeria innocua respectively) of the validation points outside the predic-

tion region. A possible explanation is that validation data points obtained at

different time points from a single experiment are not totally independent, an

assumption which was made for the Monte Carlo simulation of the prediction

boundaries. Once a growth curve is evolving somewhat slower or somewhat

faster, it will keep on evolving slower or faster for the rest of the experiment.

Analysis and modelling of high oxygen effects on microbial growth 81

A ‘risk area’ was calculated by combining the overall growth model for L.

innocua with growth model #1 for P. fluorescens. When an initial contamina-

tion level of Pseudomonaceae of 5 log cfu g−1 is assumed according to Debevere

et al. (2006), the application of oxygen concentrations above 60% to 70% imply

a potential health risk, due to a dangerous level of the pathogen on a non-spoiled

product (see Figure 3.8). When applying oxygen concentrations below 60%, at

least 1 to 2% carbon dioxide should be added to the atmosphere in order to

provide a safe storage atmosphere. This is due to the fact that the growth of

P. fluorescens was retarded by both oxygen and carbon dioxide, whereas only

carbon dioxide had an effect on the growth of L. innocua. Safe application of

oxygen concentrations up to 62% is possible when combined with CO2 levels

above 2%. We would like to draw the attention on the fact that the risk area is

based on in vitro growth models. As could be seen from the overall validation,

L. innocua grew considerably slower on fresh-cut butterhead lettuce than on

artificial growth medium. Therefore, the risk area has to be seen as a worst

case scenario, taking into consideration the in vitro growth of L. innocua which

was faster than the observed growth on the produce.

3.4.3 Growth on fresh-cut butterhead lettuce and overall

model errors

3.4.3.1 Pseudomonas fluorescens

Pseudomonaceae were the predominant group of the aerobic mesophilic bac-

teria on fresh-cut butterhead lettuce (see Figures 3.13 and 3.14). This is in

agreement with counts on minimally processed salads (Nguyen-the and Carlin,

1994) where the majority of aerobic mesophilic bacteria belonged to the group

of Pseudomonaceae and 50 to 90% of the latter group could be identified as

P. fluorescens. The growth of the GFP tagged Pseudomonas fluorescens mu-

tant on fresh-cut butterhead lettuce was in good agreement with the predicted

growth. The retarding effects of both high oxygen and carbon dioxide on the in

vitro growth of Pseudomonas fluorescens were also valid for the growth of the

bacteria on fresh-cut butterhead lettuce. The parameter %Dcount was equal to

68%. To interprete this ‘overall’ %Dcount it has to be compared to the ‘primary’

%Dcount as calculated on the in vitro validation growth data. With 66% this

‘primary’ %Dcount was in the same order of magnitude of the ‘overall’ percentage

discrepancy. Also the amount of in vivo validation data points lying outside the

95% prediction limits (11%) is low when compared to 16% of in vitro validation

points. Since overall errors were comparable to primary errors we can assume

82 3.4 Discussion

that the errors due to the growth substrate and the error due to microbial diver-

sity were negligible for the case of P. fluorescens. The model overpredicted the in

vivo growth (%Bcount was 31%) which means that the bacteria grew somewhat

slower on fresh-cut butterhead lettuce than on the nutrient agar plates. As can

be seen from Figure 3.13 not only the GFP tagged mutants grew as predicted.

The predictive model was also valid for the Pseudomonas species with which the

butterhead lettuce was originally contaminated, although a little less accurate

as can be seen from the validation parameters in Table 3.7. This means that

our proposed predictive model is generally applicable to Pseudomonas species

present on fresh-cut butterhead lettuce.

3.4.3.2 Listeria innocua

In agreement with the in vitro results, oxygen did not influence the in vivo

growth of Listeria innocua. However, unlike for Pseudomonas fluorescens, the

growth of Listeria innocua on fresh-cut lettuce did not agree with the predicted

growth. The in vivo growth was much slower than predicted (%Bcount was 98%)

and the effect of carbon dioxide turned from an inhibiting effect under in vitro

circumstances to a growth promoting effect when studied on fresh-cut lettuce.

Two factors may play a role in the explanation of this change of CO2 effect: first,

the different growth substrate and second, the microbial diversity and herewith

linked microbial interactions on fresh-cut butterhead lettuce which were not

taken into account in the development of the model.

Fresh-cut butterhead lettuce is different from nutrient agar as growth sub-

strate in the following aspects: the composition and structure, the availability

of nutrients, pH and water activity. Bacterial growth on vegetables occurs on

the surface, with typically a colonial growth. For fresh-cut vegetables, growth

will mainly occur on the cut surfaces, where the cells are ruptured and the

nutritious cell content becomes readily available. Growth on agar plates was

used in the model system to simulate this surface growth, however, diffusion

limitations are greater at a vegetable surface than within a gel (Brocklehurst,

2004). To account for the poor nutritional quality of lettuce, nutrient agar was

chosen as cultivation medium. Water activity (aw) of both nutrient agar and

vegetables is higher than 0.98 and not considered limiting for the growth of the

bacteria. Despite this application-based choice of the artificial growth medium,

differences between the model system and the real food situation are inevitable.

An aspect that needs special attention is the pH change of the growth substrate

as a result from application of CO2.

CO2 interferes with metabolic processes by direct inhibition of enzymes or

Analysis and modelling of high oxygen effects on microbial growth 83

decrease in the rate of enzyme reactions. The combined effect of these metabolic

interferences are thought to constitute a stress to the system (Farber, 1991).

According to Devlieghere et al. (1998) CO2 exerts its antimicrobial effect in the

aqueous phase of the food product. In our experiments, the dissolved CO2 is

dependent on the pH of the growth substrate, the partial pressure of CO2 in

the gas mixture and the temperature. Since the pH of the lettuce is lower than

the pH of the nutrient agar in the beginning of the experiment, less CO2 would

be dissolved that can exert its antimicrobial effect.

Besides the direct effect that dissolved CO2 exerts on bacterial growth, dis-

solved CO2 also dissociates in aqueous media and forms carbohydrate ions and

protons, with acidification of the medium as a result. As could be seen from

the pH experiment, the pH of nutrient agar drastically decreased under CO2

treatment, whereas the pH of cut lettuce increased. This increase in pH was

also observed by Siriphanich and Kader (1986) on different lettuce cultivars

(also regardless the CO2 concentration applied) and suggested that a mecha-

nism regulating pH might be operating under high CO2 concentration. These

authors stated that a drop in pH could be prevented by the activation of malic

enzyme under acidic conditions which catalyses the decarboxylation of malate

into pyruvate, CO2 and OH−.

Since the pH of nutrient agar decreased drastically, the CO2-effect as it was

incorporated in the predictive model is thus a combination of a pure CO2-effect

and a pH effect. In cut lettuce the pH was buffered to 6.6. Possibly, the

unexpected slow growth of L. innocua on cut lettuce stored at gas atmospheres

without CO2 was due to the low pH (6.2) of the lettuce which only increased

to 6.6 units after 4 to 7 days. Under these conditions, the pH of nutrient

agar was 7. An explanation of the effects at elevated CO2 levels becomes more

complex, since the changing pH values also have an effect on the solubility of

CO2. Moreover, the same pH changes applied for the P. fluorescens experiment,

where the growth in vivo was comparable to in vitro. Possibly, differences in

pH sensitivity may play a role here, but it is clear that for a full understanding

of the L. innocua growth on lettuce, additional experiments should be carried

out, monitoring growth on buffered media.

In addition to the above mentioned pH effects, other factors might influence

the contradiction of the in vitro and in vivo CO2 effect. Microbial diversity is,

next to the growth substrate, the main difference between a model system and

a real food situation. Competition between micro-organisms on a solid food

matrix depends on the proximity of the colonies to each other. Competition

can be due to nutrient depletion, changes of the chemical composition of the

84 3.4 Discussion

surface or due to the production of bactericidal agents. During the first in vivo

experiment on L. innocua, the growth of lactic acid bacteria and the aerobic

mesophilic count was monitored.

The growth of lactic acid bacteria (LAB) was faster at gas conditions with

15% CO2 compared to 0% CO2, whereas the growth of aerobic mesophilic bac-

teria (AMC) was retarded at conditions with 15% CO2. It is generally accepted

that CO2 has a greater inhibitive influence on gram-negative bacteria (e.g. pseu-

domonads being the most important representative of the aerobic mesophilic

group) than on gram-positive bacteria (LAB) (Francis and O’Beirne, 1998).

Also, the growth of the LAB could have been faster at 15% CO2 than at 0%

CO2 because of less nutritional competition with the prominently present AMC

in the latter situation. The general statement that the low temperatures and the

gas conditions in classic low oxygen MAP favours the growth of LAB (Nguyen-

the and Carlin, 1994) thus also applies here when it concerns the combination

of CO2 and high O2. Lactic acid bacteria are believed to exert antimicrobial ef-

fects due to pH changes, generation of hydrogen peroxide, nutrient competition

or production of bacteriocins e.g. antilisterial compounds (Francis et al., 1999).

However, since the growth of L. innocua was favoured by the same conditions

as the LAB, we do not believe competition with LAB is the explanation for the

reversed CO2-effect on L. innocua in vivo.

More probably, the faith of L. innocua was closely related to the most promi-

nent group of spoilage bacteria on the lettuce: the Pseudomonaceae. Bennik

et al. (1996) found that the addition of strains of fluorescent pseudomonads

slightly reduced the final population density of L. monocytogenes on endive leaf

medium. Also Enterobacteriaceae (also belonging to the AMC) would form a

serious nutritious competition for L. innocua. The in vivo growth of L. innocua

was especially slower on lettuce in comparison to in vitro at conditions where

the AMC was not affected (gas conditions without CO2). On the other hand,

Listeria growth in vivo was similar to predicted growth based on the in vitro

models under gas conditions where the AMC was suppressed (15% CO2). More

rapid growth of L. monocytogenes at increased CO2 concentrations (10%, 30%

and 50%) has also been reported in combination with 10% O2 on chicory leaves

(Carlin et al., 1996). Similar effects were previously described on other foods

as the Jameson effect (Jameson, 1962). A quantification of this effect was re-

alised here by predicting the time to reach a maximum population density of

pseudomonads, using the P. fluorescens growth model. Total growth inhibition

of L. innocua was considered after this time. The resulting growth prediction

was in better agreement with the observed growth of L. innocua. For a better

Analysis and modelling of high oxygen effects on microbial growth 85

quantification of the effect, the growth of Pseudomonaceae could be followed on

the lettuce inoculated with L. innocua.

According to Francis et al. (1999), classic low oxygen MA packaging of re-

frigerated ready-to-use vegetables might be cause for public health concern for

three reasons: the applied growth conditions may first, inhibit the development

of natural competitors of pathogens, and second, increase the shelf-life which in-

creases the time available for the pathogens to grow. Third, temperature abuse

can cause anaerobic conditions which allows (facultative) anaerobe pathogens.

According to our research, these reasons also apply for high oxygen + CO2 gas

conditions, except for the third reason. The health risk is mainly linked to the

increased CO2 concentration, as well in low O2 as high O2 MAP.

3.5 Conclusions

The applied gas atmosphere influences the growth of P. fluorescens, a typical

vegetable spoilage organism and L. innocua, a model organism for the foodborn

pathogen L. monocytogenes. Where the effect on the in vitro growth of L. in-

nocua could be attributed to an inhibitive effect from carbon dioxide, the in

vitro growth of P. fluorescens was inhibited by elevated CO2 and high O2 con-

centrations. When the reported gas effects on lag time and maximum specific

growth rate were incorporated in a Baranyi-model, overall models were obtained

that successfully described the in vitro bacterial growth as a function of time,

oxygen and carbon dioxide concentration. The growth of an inoculated GFP-

tagged strain of Pseudomonas fluorescens on fresh-cut butterhead lettuce was

very similar to what was predicted with the model. More general, the growth of

the pseudomonads that naturally contaminated the lettuce also agreed with the

model prediction. In contrast, the behaviour of L. innocua on fresh-cut lettuce

was not as predicted. Growth was considerably slower than in vitro especially

at conditions without CO2. Instead of exerting a growth retarding effect as

in vitro, CO2 had a growth promoting effect on L. innocua when studied on

fresh-cut lettuce. It was hypothesised that the discrepancy between in vitro and

in vivo growth of L. innocua resulted from interactions with pseudomonaceae,

the predominant bacterial group on cut lettuce. Under conditions with 15%

CO2, where pseudomonads were retarded, growth of L. innocua was according

the model predictions based on in vitro experiments. Under conditions with-

out CO2, growth of L. innocua was considerably slower than predicted, which

was assumed to be caused by competition with pseudomonads. These effects

were quantified in a model validation step incorporating a Jameson effect in the

86 3.5 Conclusions

model. The favorisation of L. innocua in high oxygen atmospheres in combina-

tion with elevated CO2 may imply a potential health risk, but not to a larger

extent than for low O2 modified atmospheres.

Chapter 4

Analysis and modelling of

high oxygen effects on

enzymatic brown

discoloration

4.1 Introduction

Of concern for fresh-cut produce is the rapid enzymatic browning due to the

mechanical damage to the tissue. An analysis of the various components of

overall visual quality showed that surface and edge browning were the defects

which most contributed to a decrease in quality of different lettuce types (Lopez-

Galvez et al., 1996). Due to the increasingly dismissive attitude of the consumers

to chemical treatments e.g. sulphite compounds, there is a need to find alter-

native, natural treatments to control enzymatic brown discoloration (Lu and

Toivonen, 2000). Besides treatments with organic acids, natural essential oils

or antioxidants, browning can be retarded by the use of modified atmosphere

packaging (Laurila et al., 1998). In classic MA packages, a delayed browning is

accomplished by a reduction of the oxygen concentration. However, an exces-

sive decrease of oxygen levels could induce anaerobic metabolism, physiological

damage and off-flavour development (Day, 1996). The use of superatmospheric

oxygen concentrations has been found to reduce enzymatic browning reactions

in various products. The appearance scores of shredded iceberg lettuce stored

at 80% O2 were higher than those at 20% O2 because of a reduction in browning

87

88 4.1 Introduction

(Barry-Ryan and O’ Beirne, 1998), but no further quantification of the reduc-

tion was given. Until 30 days of storage, peel browning of longan fruit was

completely avoided at 70% O2, whereas in other CA treatments (4% O2 + 5%

CO2 and 4% O2 + 15% CO2) visual browning occured after 20 days (Tian et al.,

2002). Lu and Toivonen (2000) pretreated ‘Spartan’ apples for 5 days at 1 ◦C

with 1, 21 or 100 kPa O2 before slicing. Apple slices of apples pretreated with

100 kPa O2 browned less than those pretreated with 1 or 21 kPa of O2 according

to the L∗-values measured on the apple slices (75, 67 and 62 with pretreatment

with 100, 1 or 21 kPa O2, respectively).

Browning is the consequence of a chain of reactions in which the first step is

catalyzed by the enzyme phenylalanine ammonia lyase (PAL, EC 4.3.1.5) which

degrades phenylalanine to ammonia and cinnamic acid. Cinnamic acid is hy-

droxylated, its aromatic group is methylated and the carboxyl group reduced to

obtain phenolic compounds (Siriphanich and Kader, 1985). These compounds

are oxidised by polyphenol oxidase (PPO, EC 1.14.18.1). Peroxidases (POD;

EC 1.11.1.7) are of less importance since the low level of H2O2 in plants limits

peroxidase activity. PPO catalyses two different types of reactions: the hydrox-

ylation to the o-position adjacent to an existing hydroxyl group of the phenolic

substrate (monophenol oxidase activity), and the oxidation of diphenol to o-

benzoquinones (diphenol oxidase activity). The reactions use molecular oxygen

as a secondary substrate (Burton, 2001) and need copper (Shi et al., 2002).

PPO may exhibit both mono- and diphenol oxidase activity. However, when

both activities are present, the ratio is usually 1:10 or as low as 1:40 (Marshall

et al., 2000). The enzymatically formed o-quinones react immediately, mainly

non-enzymatically, with other quinones, phenols and amino compounds to pro-

duce melanins, the pigments responsible for the brown colour (Laurila et al.,

1998).

PPO is synthesised in the cytosol, imported into plastids or incorporated

into the thylakoidal membrane of chloroplasts and processed to a mature form

(Murata et al., 1998), while their phenolic substrates are mainly located in the

vacuole. Consequently, enzymatic browning only occurs when the cell structure

is damaged.

Studies related to PPO activity dealing with oxygen as the variable sub-

strate, especially at superatmospheric concentrations, are scarce. The activity

of PPO extracted from longan fruit and fruit peel was lowest for fruit that was

stored under high oxygen atmospheres (Tian et al., 2002; Kaewsuksaeng et al.,

2005). After 20 days, PPO activity in cv. Chuliang was about half of that

stored at 4% O2 + 15% CO2 (Tian et al., 2002). Barry-Ryan and O’ Beirne

Analysis and modelling of high oxygen effects on enzymatic brown discoloration 89

(1998) mentioned that PPO activity was inhibited non-competetively by high

oxygen in preliminary enzyme assays but further details were not given. It has

been suggested that high O2 concentrations may cause substrate inhibition of

PPO or, on the other hand, that high levels of formed quinones would generate

feed-back product inhibition (Day, 1996). But until now, not much is known

about the real mechanism by which high oxygen concentrations inhibit PPO

activity.

The first purpose of this work was to describe the in vitro kinetics of PPO

with respect to oxygen concentrations from 5 to 100%, and to develop a model

for the reaction kinetics of mushroom PPO using chlorogenic acid (CGA) as sub-

strate. Mushroom PPO was used since it is a ‘model’ tyrosinase previously used

in many works in which kinetic and structural features of tyrosinase have been

approached (Espın and Wichers, 2001; Lerici and Manzocco, 2000; Weemaes,

1998; Rodrıguez-Lopez et al., 1992). CGA is the predominant phenolic com-

pound in some fruit and vegetables like lettuce, endive, celery, eggplant, potato,

apple, pear, peach, apricot, raspberry and coffee (de Rigal et al., 2000; Maz-

zafera and Robinson, 2000; Janovitz-Klapp et al., 1990; Ferreres et al., 1997;

Gonzalez et al., 1999). CGA has also been described as one of the best PPO

substrates in vitro (Heimdal et al., 1997). The models need to take into account

possible oxygen and product inhibition effects.

In the second part of this chapter, brown discoloration of fresh-cut butter-

head lettuce stored at high oxygen and CO2 atmospheres will be evaluated.

Since colour measurements may be hampered by the heterogeneous nature of

cut lettuce, an objective method to measure colour needed to be developed. The

next objective of this part is to model colour changes over time as influenced by

the storage atmosphere.

The chapter consists of a section devoted to the description of all materi-

als and methods (4.2), a results section (4.3), a discussion section (4.4) and

a conclusions section (4.5). Except for the latter, all sections of this chapter

are divided in two subsections: one describing the in vitro PPO assay and one

describing the colour measurements on fresh-cut butterhead lettuce.

The results presented in this chapter are partly published in Gomez et al.

(2006).

90 4.2 Materials and Methods

Magnetic stirrer

Water in

Water out Reactionvolume

Oxygen electrode

Mixing bar

Screw

Figure 4.1: Schematic representation of the bioreactor with built-in Clark type O2-

electrode.

4.2 Materials and Methods

4.2.1 In vitro PPO assay

4.2.1.1 Bioreactors

PPO activity was determined by measuring the oxygen consumption during the

enzymatic reaction. A small bioreactor according to Inoue (1989) with a built-in

Clark type O2-electrode was used. In Figure 4.1 a schematic representation of

the bioreactor is given. The bioreactor was equipped with a water jacket which

was connected to a water bath (Haake F6 C25) to keep a constant tempera-

ture of 25 ◦C (± 0.05 ◦C). During the experiments, the reactor volume (5 ml)

was continuously and smoothly stirred in order to facilitate permeation of O2

through the electrode membrane made of Teflon® and to have fast equilibrium

between the oxygen concentration in the reactor and the oxygen concentration

at the electrodes. Applying a voltage of 700 mV over the electrodes resulted in

a current proportional to the oxygen concentration. This current was measured

every 10 seconds with a data acquisition unit (Agilent 34970 A).

4.2.1.2 Standard assay conditions

The reactor was filled with a 4 ml solution of 30 mM CGA (minimum 95%,

C3878, Sigma-Aldrich) dissolved in phosphate buffer (0.1 M, pH 6.5). The zero

O2 reading was obtained by adding a small amount of sodium dithionite to

eliminate O2. Other calibration points at 25 ◦C were obtained by bubbling the

reactor for 300 s at a flow rate of 1.38 ml s−1 with different gas mixtures (5, 10,

15, 20, 40, 60 and 100% O2, N2 to balance). Pure gases (oxygen, carbon diox-

ide and nitrogen) were mixed using mass-flow controllers (model 5850S, Brooks

Instrument, The Netherlands). O2 and N2 concentrations of the gas mixtures

were checked with a micro GC (CP2003-P, Varian-Chrompack, Bergen op Zoom,

Analysis and modelling of high oxygen effects on enzymatic brown discoloration 91

The Netherlands). These gas mixtures resulted in oxygen concentrations in the

solution of 0.064, 0.129, 0.193, 0.258, 0.515, 0.773 and 1.288 mM respectively,

according to Henry’s law. 40 µl of PPO (mushroom tyrosinase, EC 1.14.18.1,

T7755, Sigma-Aldrich) dissolved in phosphate buffer (0.1 M, pH 6.5) at a con-

centration of 0.08 mg ml−1 (230 units ml−1) was added to the saturated solution

to start the reaction. Commercially available mushroom PPO possibly contains

different isozymes of PPO. Derived reaction kinetics therefore will have to be

interpreted as the result of the action of different isozymes which is consid-

ered representative for the situation in real systems. One unit of the enzyme

is defined as the amount of enzyme that will cause an increase in A280nm (ab-

sorbance at 280 nm) of 0.001 per minute at pH 6.5 at 25 ◦C in a 3 ml reaction

mix containing L-tyrosine. The cuvette was tightly closed and the O2 concen-

tration was measured every 10 seconds. The measurements were stopped after

20 equal consecutive voltage readings which were considered as indicators of a

complete O2 consumption. Before and after one (initial O2 concentrations of

0.515, 0.773 and 1.288 mM) or two experiments (initial O2 concentrations of

0.064, 0.129, 0.193 and 0.258 mM), a calibration was performed in the O2 range

of the measurement (at least 4 points) to obtain a correlation between the mea-

sured voltage and the O2 concentration. In total, 31 experiments were carried

out, having 4 repetitions for 0.064, 0.129, 0.515 and 0.773 mM and 5 repetitions

for 0.193, 0.258 and 1.288 mM initial O2 concentration respectively.

4.2.1.3 Kinetic data analysis

Methods currently used to determine enzyme kinetics involve the calculation of

the slopes of the linear zone of substrate or product concentration change curves.

In a second step the slopes are regressed against substrate concentrations to de-

termine the kinetic constants (Marangoni, 2003). In this work experimental

data of so-called total depletion curves were analysed instead of the first linear

piece of the curve. Curves were fitted by a differential equation taking into

account changing substrate and changing product concentrations. This method

uses all information from one enzymatic reaction to estimate the kinetic charac-

teristics of the enzyme. The Michaelis-Menten parameters are estimated in one

step instead of calculating the parameters indirectly from different enzymatic

reactions. The method is especially advantageous when characterisation of the

enzyme with the classic method is difficult due to low Km values.

The reaction catalysed by PPO is described by equation (4.1).

2S + O2kcat−→ 2P + 2H2O (4.1)

92 4.2 Materials and Methods

S is the substrate which is CGA, P is the product which is the oxidised form

of CGA and kcat is the reaction rate constant. It is assumed that the substrate

S is available in a non limiting concentration. Furthermore it is assumed that

there is an excess amount of substrate so that the substrate concentration [S]

can be considered as constant. As there is O2 consumed in the reaction, the

change of the O2 concentration [O2] with time t was used as a measure for the

reaction rate v.

−d[O2]dt

= v (4.2)

The minus sign in equation (4.2) makes the reaction rate v positive as the

oxygen concentration is decreasing during the reaction. From equation (4.1) it

can easily be seen that the change in concentration of the product P is given

by:

d[P]dt

= 2v (4.3)

as one oxygen molecule produces two molecules P. By substituting equation

(4.2) in equation (4.3) and solving the resulting differential equations for the

initial conditions ([P] = 0 and [O2] = [O2]initial at t = 0), it is straightforward

to see that

[P] = 2([O2]initial − [O2]) (4.4)

Equation (4.4) holds for all times and all experimental conditions carried

out.

To describe the reaction rate v, Michaelis-Menten kinetics were applied

v = Vmax[O2]

Km + [O2](4.5)

Vmax is the maximum reaction rate and Km is the Michaelis-Menten rate

constant of the enzymatic reaction. It has been assumed that the total enzyme

concentration remains constant during the experiments.

Solving differential equation (4.2) using equation (4.5) for the reaction rate

and fitting the solution to each depletion curve separately results in a set of

(Vmax, Km) couples. Vmax and Km were plotted as a function of the ini-

tial oxygen concentration [O2]initial to decide on suitable inhibition kinetics.

Four different inhibition types according to Marangoni (2003) were tested. A

competitive inhibition results in increasing apparent Km values with increasing

inhibitor concentration while Vmax stays constant. The reaction rate is then

described by

Analysis and modelling of high oxygen effects on enzymatic brown discoloration 93

v = Vmax[O2]

Km(1 + [I]/Kmc) + [O2](4.6)

with Kmc the Michaelis-Menten constant for competitive inhibition, and [I]

the inhibitor concentration. Uncompetitive inhibition gives rise to decreasing

apparent Km and Vmax values with increasing inhibitor concentration. The

reaction rate for uncompetitive inhibition is given by

v = Vmax[O2]

Km + [O2](1 + [I]/Kmu)(4.7)

with Kmu the Michaelis-Menten constant for uncompetitive inhibition. Non-

competitive inhibition will result in decreasing apparent Vmax values while Km

stays constant with increasing inhibitor concentration:

v = Vmax[O2]

(Km + [O2])(1 + [I]/Kmn)(4.8)

with Kmn the Michaelis-Menten constant for non-competitive inhibition.

Finally, a linear mixed inhibition model where the inhibitor acts in both a

competitive and uncompetitive way can be employed:

v = Vmax[O2]

Km(1 + [I]/Kmc) + [O2](1 + [I]/Kmu)(4.9)

Kmc is the Michaelis-Menten constant for competitive inhibition and Kmu

is the Michaelis-Menten constant for uncompetitive inhibition. One by one the

four inhibition kinetics (equations (4.6)-(4.9)) were incorporated in equation

(4.2) and their ability to describe all the depletion curves with one set of pa-

rameters, was evaluated. Both [O2] and [P] were tested as possible inhibitors.

An adaption of the linear mixed inhibition model (Equation (4.9)) was made,

where oxygen was considered as an uncompetitive and the product as a com-

petitive inhibitor of PPO. This yields the model described in equation (4.10).

For more information on the development and the enzymatic background of this

equation the reader is referred to Appendix 1.

v = Vmax[O2]

Km(1 + [P ]/Kmc) + [O2](1 + [O2]/Kmu)(4.10)

To further differentiate the results and to decide on oxygen or product as

being the inhibitor, a plot showing the reaction rate as a function of prod-

uct concentration for four different levels of oxygen concentrations (0.064 mM

(5%), 0.258 mM (20%), 0.773 mM (60%) and 1.288 mM (100%)) was con-

structed. From each depletion curve small nearly linear parts corresponding to

the four oxygen levels were selected. The slopes of these parts were considered

94 4.2 Materials and Methods

to represent the reaction rate of the enzyme at the respective oxygen concentra-

tion. The product concentrations at these parts were calculated using equation

(4.4). Each slope was calculated by linear regression of 20 consecutive oxygen

measurements (measurements every 10 seconds).

4.2.1.4 Numerical solution and fitting procedure

Differential equations were solved using a Matlab® program (The MathWorks,

Natick, USA). The program used ODE45 as a variable-step continuous solver

for numerical integration. The solutions gave the oxygen concentration as a

function of time during a full depletion curve on which they were fitted using

a least squares criterion. The least square optimisation was carried out using

the Matlab® least square non linear optimisation routine (lsqnonlin) with the

Levenberg-Marquardt method. The program returned predicted values, resid-

uals and other statistical output as the approximate standard errors, and the

95% confidence intervals of the kinetic parameters. The kinetic models were

compared based on the goodness-of-fit parameters R2adj and root mean squared

error (RMSE).

4.2.2 Brown discoloration of fresh-cut butterhead lettuce

4.2.2.1 Development of method to measure colour changes in cut

lettuce

The first objective was to develop an objective colour measurement for fresh-cut

butterhead lettuce. For this purpose, butterhead lettuce (Lactuca sativa L.) cv

‘Zendria’ was cut (as previously explained in section 2.2.1.1) and stored at 5 ◦C

for 8 days at 4 different gas conditions ((% O2: % CO2), balance nitrogen):

(20:0); (20:10); (75:0); (75:10). Additionally, disks of 3 mm thickness were hand

cut from the first 10 mm of the lettuce stem using a sharp knife. Immediately

the disks were immersed for 1 min in tap water at 5 ◦C and centrifuged using

a domestic centrifuge. The disks were stored at the same conditions as the cut

lettuce, but in a separate jar and on a grid to ensure contact of upper- as well as

downside of the disk with the gas mixture (see Figure 4.2). Before storage and

after 5 and 8 days of storage, colour of the cut lettuce and the stem disks was

measured with a spectrophotometer (Minolta CR-300, Ramsey, NY, USA) (see

Figure 4.3). The colour measurements were based on CIE-lab values. Lightness

was expressed as the L*-value on a scale from 0 to 100. Eleven disks per gas

condition were measured in the middle of the stem at both sides. The cut

lettuce was taken from the jar and 30 (on day 0) or 90 (on day 5 and 8) colour

Analysis and modelling of high oxygen effects on enzymatic brown discoloration 95

Figure 4.2: Part of the experimental setup of jars at different gas conditions contain-

ing labeled stem disks put on grids.

measurements were done on different areas.

At day 0 and 8, sensory evaluation was carried out by 6 judges of an un-

trained panel. The judges were asked to score the cut lettuce for browning on

a scale from 1 to 5 (1= no browning; 5=severe browning). Each assessor was

characterised by his own score-vector, which consists of five scores (one score

at day 0 and four scores at day 8). Although the assessors were asked to score

on a fixed predefined scale, the average scoring position on a line scale may be

different from one assessor to the other, the so-called level effect. Another indi-

vidual scaling effect is the range-effect. This means the assessors use different

ranges of scoring. In a procrustes analysis, level-effects and range-effects were

corrected for by application of respectively a translation and isotropic scaling

(Dijksterhuis, 1996). The scores of the assessors were scaled using the ‘pro-

crustes’ command in Matlab®, in order to correct for differences in scoring

behaviour.

4.2.2.2 Consecutive stem colour measurements

Stem disks of butterhead lettuce cv ‘Zendria’ were prepared as explained in

Section 4.2.2.1. Eighteen disks per gas condition were stored in glass jars at

7 ◦C for 10 days. The following gas conditions were applied ((% O2: % CO2),

balance nitrogen): (21:0); (5:0); (5:15); (75:0); (75:15). At day 0, 1, 2, 3, 4, 5, 6,

7 and 10 the colour of the disks was measured in the middle of the disks and at

both sides. The stem disks were labelled as can be seen in Figure 4.2 in order

to follow the colour changes of individual stem disks.

96 4.2 Materials and Methods

Figure 4.3: Colour measurements with the spectrofotometer (Minolta CR-300) on

cut lettuce leaves (left) and lettuce stem disks (right).

4.2.2.3 Modelling colour changes

As previously mentioned, PPO is one of the key enzymes in enzymatic browning

reactions. An autocatalytic PPO based browning model, originally developed

to model brown discoloration of mushrooms (Bobelyn et al., 2006) was used.

In this model, the PPO catalysed oxidation of diphenols (DP) to quinones (Q)

was simplified as follows:

PPO + DPkbrown−→ Q + PPO (4.11)

Additionally, the model incorporated the activation of a latent inactive form

of PPO. Releasing endogenous fatty acids from membranes has been shown

to rapidly and completely activate this latent PPO (Golbeck and Cammarata,

1981). The activation of the inactive PPO precursor (P) by fatty acids (FA)

was modelled as:

P + FAkP P O−→ PPO + FA (4.12)

During senescence free radicals (FR) accumulate contributing to an autocat-

alytic breakdown of cell membranes (CM) releasing increasing amounts of fatty

acids (del Rıo et al., 1998; Thompson et al., 1998) which can be described as:

CM + FRksen−→ 2FR + FA (4.13)

Analysis and modelling of high oxygen effects on enzymatic brown discoloration 97

During senescence there are several processes together realising the auto-

catalytic effect on PPO activity: (i) due to membrane damage an increasing

amount of FR is produced further destabilising the membranes and releasing

more FA, (ii) the FA released will activate increasing amounts of inactive P into

active PPO, (iii) the loss of compartmentalisation provides the activated PPO

increasing access to its substrate (DP). However, this last aspect is not explic-

itly incorporated in the current model approach. As the pool of lipids coming

from cell membranes is available in excess, CM can be assumed constant and

non rate limiting. This results in the following model description (4.14):

d[PPO]/dt = kPPO[P ][FA]

d[P ]/dt = −kPPO[P ][FA]

d[Q]/dt = kbrown[PPO][DP ]

d[DP ]/dt = −kbrown[PPO][DP ] (4.14)

d[FA]/dt = ksen[FR]

d[FR]/dt = ksen[FR]

Lettuce disk colour (measured as L∗) decreases from its initial colour (L∗0)

to some bottom threshold value (L∗+∞). Lettuce disk colour is assumed to be

negatively related to the amount of quinones present:

L∗ = L∗0 − a[Q] (4.15)

At t = +∞ equation (4.15) can be written as:

L∗+∞ = L∗0 − a[Q]+∞ (4.16)

As can be seen from reaction 4.11, diphenols are assumed to be completely

converted to quinones. At t = +∞, the total amount of formed quinones

([Q]+∞) is equal to the initial amount of diphenols ([DP ]0), which leads to:

a =L∗0 − L∗+∞

[DP ]0(4.17)

Replacing a in equation 4.15 by the expression in equation 4.17 leads to:

L∗ = L∗0 − (L∗0 − L∗+∞

[DP ]0)[Q] (4.18)

The initial values (at t = 0) of the set of differential equations (4.14) are

defined as follows:

98 4.2 Materials and Methods

[PPO] = PPO0 = 0

[P ] = P0 = 1

[Q] = Q0 = 0 (4.19)

[DP ] = DP0 = 0.001M

[FA] = FA0 = 0

[FR] = FR0

The initial amount of precursor (P0) was set to an arbitrary relative value

of 1. DP0 is calculated as the average phenolic acid content (391 µg g−1 fresh

weight) measured in white (213 µg g−1 fresh weight) and green lettuce tissue

(570 µg g−1 fresh weight) by Ferreres et al. (1997). Chlorogenic acid can be

considered as the most significant fraction of phenolic acids in lettuce (Ferreres

et al., 1997) and one of the best PPO substrates (Heimdal et al., 1997), so the

molecular weight of chlorogenic acid (354.3) was used (together with lettuce

density of 880 kg m−3 as previously measured) to calculate the initial concen-

tration of diphenoles in moles l−1. The resulting value of 0.001 M was used as

value for DP0.

For constant environmental conditions, equation (4.14) can be simplified

to the following differential equation describing L∗ as a function of time (see

Appendix 2):

dL∗/dt = kbrownP0

(1− exp

(kPPOFR0

ksen

(1− eksent

)+ kPPOFR0t

))· · ·

×(L∗+∞ − L∗

)(4.20)

Differential equations were solved using a Matlab® program (The Math-

Works, Natick, USA). The program used ODE45 as a variable-step continuous

solver for numerical integration. The solutions gave the L∗-value as a func-

tion of time. The least square optimisation was carried out using the Matlab®

least square non linear optimisation routine (lsqnonlin) with the Levenberg-

Marquardt method. The program returned predicted values, residuals and other

statistical output as the approximate standard errors, and the 95% confidence

intervals of the kinetic parameters. The kinetic models were compared based

on the goodness-of-fit parameters R2adj and root mean squared error (RMSE).

Analysis and modelling of high oxygen effects on enzymatic brown discoloration 99

0 0.3 0.6 0.9 1.2 1.50

0.05

0.1

0.15

0.2

0.25

0.3

Initial oxygen concentration (mM)

Km

(m

M)

Figure 4.4: Estimation of apparent Km values of PPO and corresponding approx-

imate 95% confidence limits as a function of initial O2 concentration and resulting

from the Michaelis-Menten equation fitted for each experiment separately.

4.3 Results

4.3.1 In vitro PPO activity

The Michaelis-Menten model (equation (4.5)) fitted on the data of every exper-

iment separately was adequate to explain O2 consumption during oxidation of

CGA by PPO. The averaged R2adj per initial O2 concentration were high (be-

tween 0.89 and 0.99). However, when plotting both the resulting Km and Vmax

values as functions of the initial O2 concentration, they followed a trend instead

of being constant through all the experiments. There was an effect, apparently

associated with the presence of an inhibitor, which determined an increase in

Km and a decrease in Vmax (Figures 4.4 and 4.5). Significant differences between

kinetic parameters Vmax and Km were evident from their confidence limits and

thus basic Michaelis-Menten kinetics were not obeyed. Because of this reason,

the obtained Km and Vmax values were considered as apparent values.

Four inhibition models (equations (4.6) to (4.9)) considering both substrate

(O2) and product as inhibitors were fitted to all depletion curves together. The

resulting R2adj values were high and RMSE values were small for all eight inhibi-

tion cases and on itself did not give enough information to select the appropriate

inhibitor and/or inhibition kinetics (Table 4.1). Nevertheless, from the theoret-

100 4.3 Results

0 0.5 1 1.50

1

2

3

4x 10

−4

Initial oxygen concentration (mM)

Vm

ax (

mM

.s−

1 )

Figure 4.5: Estimation of apparent Vmax values of PPO and corresponding approx-

imate 95% confidence limits as a function of initial O2 concentration and resulting

from the Michaelis-Menten equation fitted for each experiment separately.

ical point of view, the way the kinetic parameters were affected (decrease in

Vmax and increase in Km, Figure 4.4 and 4.5) can only be described by the

linear mixed or combined inhibition model (see Appendix 1).

To confirm and to elucidate which factor was the inhibitor, oxygen or prod-

uct, PPO reaction rates at four O2 concentrations (0.064 mM (5%), 0.258 mM

(20%), 0.644 mM (50%) and 1.288 mM (100%)) were calculated and plotted

as a function of product concentration (Figure 4.6). The results confirmed the

hypothesis that the product was inhibiting the reaction. Increasing the concen-

tration of oxidised CGA inside the bioreactor cell resulted in a lower reaction

rate (Figure 4.6). At higher product concentrations ([P] >0.5 mM) the product

inhibition seemed to be independent of the oxygen concentration. However, at

low product concentrations oxygen seemed to have an inhibiting effect as well.

At [P] = 0 the reaction rates were much higher at the low oxygen concentration

levels.

From Figure 4.6 it became clear that both oxygen and the product had

inhibiting effects. The combination of decreased apparent Vmax values and

increased apparent Km values with increasing O2 concentrations (see Figure

4.5 and 4.4) are indicative for mixed inhibition kinetics. A model consisting of

equations (4.2), (4.4) and (4.10), where O2 was considered as an uncompetitive

and the product as a competitive inhibitor, was the most appropriate to describe

Analysis and modelling of high oxygen effects on enzymatic brown discoloration 101

Table

4.1

:R

2 adj

valu

esand

root

mea

nsq

uare

der

rors

(RM

SE

)use

das

addit

ional

info

rmati

on

todet

erm

ine

the

natu

reofth

ein

hib

itio

nofP

PO

act

ivity.

Inhib

itor

Kin

etic

sE

quati

on

R2 adj

RM

SE

Com

pet

itiv

ev

=V

max

[O2]

Km

(1+

[P]/

Km

c)+

[O2]

0.9

855

0.0

435

Unco

mpet

itiv

ev

=V

max

[O2]

Km

+[O

2](1+

[P]/

Km

u)

0.9

870

0.0

435

Pro

duct

Non-c

om

pet

itiv

ev

=V

max

[O2]

Km

+[O

2](1+

[P]/

Km

n)

0.9

869

0.0

412

Lin

ear

mix

edv

=V

max

[O2]

Km

(1+

[P]/

Km

c)+

[O2](1+

[P]/

Km

u)

0.9

872

0.0

412

Com

pet

itiv

ev

=V

max

[O2]

Km

(1+

[O2]/

Km

c)+

[O2]

0.9

738

0.0

591

Unco

mpet

itiv

ev

=V

max

[O2]

Km

+[O

2](1+

[O2]/

Km

u)

0.9

738

0.0

591

Oxygen

Non-c

om

pet

itiv

ev

=V

max

[O2]

Km

+[O

2](1+

[O2]/

Km

n)

0.9

738

0.0

591

Lin

ear

mix

edv

=V

max

[O2]

Km

(1+

[O2]/

Km

c)+

[O2](1+

[O2]/

Km

u)

0.9

740

0.0

591

Pro

duct

Com

bin

edv

=V

max

[O2]

Km

(1+

[P]/

Km

c)+

[O2](1+

[O2]/

Km

u)

0.9

881

0.0

400

and

oxygen

102 4.3 Results

0 0.5 1 1.5 2 2.5 30

1

2

x 10−4

Product concentration (mM)

Rea

ctio

n ra

te (

mM

CG

A s

−1 )

Figure 4.6: Estimated reaction rates of PPO as a function of product concentration

for four levels of oxygen (� 0.064 mM, � 0.258 mM, N 0.644 mM and • 1.288 mM).

Table 4.2: Kinetic constants for PPO activity using Michaelis-Menten equation with

combined oxygen and product inhibition model terms (equations (4.2), (4.4) and

(4.10)).

Parameter Estimate ± approximate standard deviation

Km (mM) (3.2±1.0) × 10−3

Vmax (mM s−1) (177±3) × 10−6

Kmc (mM) 0.023±0.006

Kmu (mM) 1.63±0.10

the experimental results as based on the lowest RMSE (Table 4.1). The model

showed significant values for Kmc and Kmu (with Kmc <Kmu) indicating that

both mechanisms were present (Table 4.2). From a theoretical point of view, it

appears that the product competes with the enzyme substrate while the oxygen

interacts with the enzyme-substrate complex as the result of an uncompetitive

behaviour. Observed together with fitted oxygen concentration values for four

typical depletion curves (for 4 experiments) are shown in Figure 4.7. The model

fitted the measurements reasonably well and suggested that the used model

equations were appropriate.

Analysis and modelling of high oxygen effects on enzymatic brown discoloration 103

0 5000 10000 15000 200000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Time (s)

Oxy

gen

conc

entr

atio

n (m

M)

Figure 4.7: Observed (symbols) and fitted model (lines) values for four typical O2

depletion curves with different initial oxygen concentration values (0.064 mM with ♦

and solid line, 0.258 mM with � and dashed line, 0.515 mM with 4 and dotted line,

1.288 mM with© and dashed-dotted line). The model is described by equations (4.2),

(4.4) and (4.10).

104 4.3 Results

Day 0 75−10 20−10 75−0 20−0

1

2

3

4

5

Bro

wni

ng s

core

Condition (%O2−%CO

2)

Figure 4.8: Average browning scores and 95% confidence limits of fresh-cut butter-

head lettuce before storage (day 0) and after 8 days of storage at different gas condi-

tions (% O2-% CO2) before (in grey) and after (in white) procrustes transformation.

(1=no browning; 5=severe browning)

4.3.2 Brown discoloration of fresh-cut butterhead lettuce

4.3.2.1 Development of a method to measure colour changes in cut

lettuce

The results of the sensory evaluation of the fresh-cut butterhead lettuce be-

fore and after procrustes transformation are shown in Figure 4.8. The 95%

confidence intervals are more narrow after the procrustes transformation which

demonstrates that the variability between judges of the sensory data has been

decreased. As a result, all conditions were significantly different from each other.

Before storage, no visual browning occured. After 8 days of storage the sensory

panel judged the air-stored cut lettuce as extremely brown. A decrease in visual

browning could be achieved by application of 75% oxygen. When in addition

CO2 was added to the gas mixture, browning further decreased.

The results of the colour measurements on cut lettuce leaves and stem disks

are shown in Figure 4.9. A lower L∗-value means a more brown colour. Figure

4.10 contains pictures of cut lettuce leaves and stem disks stored for 8 days at

5 ◦C at different gas conditions. When measured on the lettuce leaves, the L∗-

value was significantly lower before storage than after 8 days of storage. After

8 days of storage the lettuce can be sorted from high to low L∗-values in the

following order: 75% O2 + 0% CO2 >75% O2 + 10% CO2 >20% O2 + 10% CO2

>20% O2 + 0% CO2. These results do not correspond with the visual browning

as scored by the assessors of the sensory panel. The L∗-values measured on

stem disks were in better agreement with the sensory scores. Lower L∗-values

measured on stem disks corresponded with higher browning scores. The relation

between L∗-values and browning scores is visualised in Figure 4.11. Based on

these results, objective browning measurements were carried out on stem disks

Analysis and modelling of high oxygen effects on enzymatic brown discoloration 105

Day 0 75−10 20−10 75−0 20−0

50

54

58

62

L* −va

lue

Condition (%O2−%CO

2)

Figure 4.9: Average L∗-value and 95% confidence limits measured on fresh-cut but-

terhead lettuce leaves (grey bars) and on stem disks (white bars) before storage (day

0) and after 8 days of storage at different gas conditions (% O2-% CO2).

Figure 4.10: Cut lettuce leaves (left) and stem disks (right) stored for 8 days at 5 ◦C

at different gas conditions (from left to right and from top to bottom consecutively:

air, 75% O2 + 0% CO2, 20% O2 + 10% CO2, 75% O2 + 10% CO2).

in the subsequent experiment.

4.3.2.2 Consecutive stem colour measurements

The average L∗-values and 95% confidence limits of the stem disk colour mea-

surements over time are represented in Figure 4.12. At the beginning of the

experiment, L∗-values were around 55. During the first two days, L∗-values

remained at the same level at all conditions. After that they quickly decreased

for conditions without CO2 (4, ×, �). Browning was retarded when 5% O2

was combined with 15% CO2 (©) and even more when 75% O2 was combined

with 15% CO2 (♦). The slower brown discoloration at these conditions resulted

from a combination of a later onset of the browning (longer shoulder) and a less

severe decrease of the L∗-value (weaker slope).

As explained in Section 4.2.2.2, the colour of individual stem disks was mon-

106 4.3 Results

1 2 3 4 554

56

58

60

62

64

Browning score

L*−

valu

e

Figure 4.11: Relation between browning scores (transformed) and L∗-values of lettuce

stem disks.

0 2 4 6 8 1035

40

45

50

55

60

Time (days)

L* −va

lue

Figure 4.12: Average L∗-value and 95% confidence limits of lettuce stem disks as a

function of storage time at different gas conditions (♦ : 75% O2 + 15% CO2; � : 75%

O2 + 0% CO2; © : 5% O2 + 15% CO2; × : 5% O2 + 0% CO2; 4 : 21% O2 + 0%

CO2).

Analysis and modelling of high oxygen effects on enzymatic brown discoloration 107

0 2 4 6 8 1020

30

40

50

60

70

Time (days)

L* −va

lue

Figure 4.13: L∗-values of stem disks stored at air conditions at 7 ◦C. Each line

connects the average L∗-values of one disk (average over the upper and down side of

the disk).

itored. This results in an individual colour profile for every stem disk as is

shown for the disks stored at air conditions in Figure 4.13. The represented

data points are the average L∗-values of both sides of the stem disk.

4.3.2.3 Modelling colour changes

At first, the autocatalytic browning model (set of equations (4.14) with start

conditions (4.19)) was fitted to the average L∗-data for each gas condition sep-

arately. This resulted in a set of estimates for kPPO, ksen, kbrown, FR0, L∗0

and L∗+∞ per gas condition. At all gas conditions there was a high correlation

between the model parameters kPPO and FR0 with ksen and L∗+∞ with kbrown.

These correlations are obvious when inspecting equation (4.20). More informa-

tion on the calculation of this equation can be found in Appendix 2. Based on

these results, fixed values were assigned to the parameter kPPO (= 1) and to

FR0 (= 0.0001) and L∗+∞ (= 40), thus leaving two parameters ksen and kbrown

and the initial browning value L∗0 to be estimated.

The model with the fixed kPPO-, FR0- and L∗+∞-values was then fitted on

the averages for each gas condition separately. The results of the model fits are

shown in Figure 4.14. As can be seen from this figure and the high R2adj-values,

the model fits were satisfactorily. At conditions with 15% CO2, the estimates

for L∗0 were somewhat higher than the measured L∗0-value. This is because L∗-

108 4.3 Results

0 2 4 6 8 10

75% O2+15% CO

2

R2adj

=0.73

Time (days)

0 2 4 6 8 10

40

45

50

55

60

5% O2+0% CO

2

R2adj

=0.99

0 2 4 6 8 10

40

45

50

55

60

L* −va

lue

21% O2+0% CO

2

R2adj

=0.97

0 2 4 6 8 1035

40

45

50

55

60

75% O2+0% CO

2

R2adj

=0.97

Time (days)

5% O2+15% CO

2

R2adj

=0.92

Figure 4.14: Average L∗-values (symbols) and 95% confidence limits of lettuce stem

disks. The lines represent the model identification curves resulting from fitting the

autocatalytic PPO-based browning model with fixed values for kPPO-, FR0- and L∗+∞

on the averages per gas condition.

values at these conditions initially increased a bit and then decreased again. The

model can only cope with this effect by choosing a higher L∗0-value. Therefore,

the L∗0-value will be estimated and not put equal to the measured L∗0.

The parameter estimates for ksen and kbrown are represented in Figure 4.15

as a function of O2 and CO2. ksen was not influenced by the O2 concentration.

At CO2 -concentrations of 15% the parameter was significanly lower than at

0%. On the contrary, kbrown was only influenced by the O2 and not by the CO2

concentration. kbrown tends to decrease with increasing O2 concentrations.

The inhibitive effect of O2 and CO2 on respectively kbrown and ksen was

consequently included in the model using equation (4.21) and (4.22):

kbrown = k′brown

11 + [O2]/KiO2

(4.21)

k′brown is independent from the gas condition and equation (4.21) is therefore

the more general expression for the ‘apparent’ rate constant kbrown. KiO2 is the

parameter describing the inhibitive effect of O2 on kbrown.

Analysis and modelling of high oxygen effects on enzymatic brown discoloration 109

0 20 40 60 800

5

10

15

O2 (%)

k sen

0 20 40 60 800

0.05

0.1

0.15

0.2

O2 (%)

k brow

nFigure 4.15: Parameters estimates and 95% confidence limits of ksen and kbrown as a

function of oxygen and carbon dioxide concentrations (� : 0% CO2 ; � : 15% CO2).

ksen = k′sen

11 + [CO2]/KiCO2

(4.22)

k′sen is independent from the gas condition and equation (4.22) is the more

general expression for the ‘apparent’ rate constant ksen. KiCO2 is the parameter

describing the inhibitive effect of CO2 on ksen.

Additionally, PPO activity could be inhibited by the reaction product, as

was shown in the in vitro PPO assays. Incorporation of this effect in the PPO

based browning model can be done using equation (4.23):

kbrown = k′brown

1(1 + [O2]/KiO2)(1 + [Q]/KiQ)

(4.23)

with KiQ the parameter describing the inhibitive effect of quinones (Q) on

kbrown.

When expressions 4.21 and 4.22 are included in model 4.14, model 4.24 is

obtained.

d[PPO]/dt = kPPO[P ][FA]

d[P ]/dt = −kPPO[P ][FA]

d[Q]/dt = k′brown

11 + [O2]/KiO2

[PPO][DP ]

d[DP ]/dt = −k′brown

11 + [O2]/KiO2

[PPO][DP ] (4.24)

d[FA]/dt = k′sen

11 + [CO2]/KiCO2

[FR]

d[FR]/dt = k′sen

11 + [CO2]/KiCO2

[FR]

110 4.3 Results

Table 4.3: Parameter estimates and corresponding approximate 95% confidence in-

tervals for the autocatalytic PPO browning model including inhition of O2 and CO2

fitted on average L∗-values.

Parameter Estimate ± 95% confidence limits

k′brown 0.14±0.02

KiO2 116±64

k′sen 6±2

KiCO2 5±2

L∗0,21−0 55.9±0.7

L∗0,5−0 55.7±0.7

L∗0,5−15 54.5±0.5

L∗0,75−0 55.5±0.7

L∗0,75−15 56.4±0.5

Table 4.4: Parameter estimates and corresponding approximate 95% confidence in-

tervals for the autocatalytic PPO browning model including inhition of O2 and CO2

fitted per stem disk.

Parameter Estimate ± 95% confidence limits

k′brown 0.03±0.02

KiO2 149±58

k′sen 9±3

KiCO2 4±2

Model 4.24 was fitted to the average L∗-values of all gas conditions together.

As in the previous model fit, fixed values were assigned to the parameter kPPO

(= 1) and to FR0 (= 0.0001) and L∗+∞ (= 40).

The model fitted the data well (see Figure 4.16, R2adj=0.96). The parameter

estimates and estimates for L∗0 are given in Table 4.3. Application of a browning

model including product inhibition, which is obtained by inclusion of expressions

4.23 and 4.22 in model 4.14, did not significantly improve the model fit to the

data.

Since stem disks were labeled during the experiment, it was possible to fit

model 4.24 on the data of individual stem disks. The L∗0-values were estimated

per stem disk. The total R2adj was 0.85. The parameter estimates are given in

Table 4.4. The model fit for 5 stem disks per gas condition is shown in Figure

4.17.

Analysis and modelling of high oxygen effects on enzymatic brown discoloration 111

0 2 4 6 8 10

75% O2+15% CO

2

Time (days)0 2 4 6 8 10

35

40

45

50

55

60

75% O2+0% CO

2

Time (days)

40

45

50

55

60

L* −va

lue

21% O2+0% CO

2

5% O2+15% CO

240

45

50

55

60

5% O2+0% CO

2

Figure 4.16: Average L∗-values (symbols) and 95% confidence limits of lettuce stem

disks. The lines represent the model identification curves resulting from fitting the

autocatalytic PPO-based browning model including O2 and CO2 inhibition terms,

with fixed values for kPPO-, FR0- and L∗+∞, on the averages over all gas condition.

112 4.3 Results

0 5 1020

40

60 1

air

0 5 10

2

0 5 10

3

0 5 10

4

0 5 10

5

0 5 1020

40

60 1

5% O2 + 0% CO2

0 5 10

2

0 5 10

3

0 5 10

4

0 5 10

5

0 5 1020

40

60

L* −va

lue 1

5% O2 + 15% CO2

0 5 10

2

0 5 10

3

0 5 10

4

0 5 10

5

0 5 1020

40

60 1

75% O2 + 0% CO2

0 5 10

2

0 5 10

3

0 5 10

4

0 5 10

5

0 5 1020

40

60 1

75% O2 + 15% CO2

0 5 10

2

0 5 10

Time (days)

3

0 5 10

4

0 5 10

5

Figure 4.17: Measured (•) L∗-values of lettuce stem disks. The lines represent the

model identification curves. The results of five disks per gas condition are given in

each row.

Analysis and modelling of high oxygen effects on enzymatic brown discoloration 113

4.4 Discussion

4.4.1 In vitro PPO activity

Day (1996) hypothesised that high O2 levels might cause substrate inhibition

of PPO or alternatively, high levels of colourless quinones subsequently formed

might cause feed-back product inhibition of PPO. This hypothesis was sup-

ported by the results in this chapter. There are not a lot of studies dealing with

the effect of O2 concentration on the PPO activity, and not much research using

superatmospheric O2 concentrations has been carried out (de Rigal et al., 2000).

Therefore results are difficult to compare. Heimdal et al. (1997) found that, ap-

parently, PPO was affected by the O2 concentration since O2 consumption in

model solutions containing CGA and purified PPO extracted from lettuce in-

creased significantly with increasing O2 concentrations from 5 to 80%. Levels

of O2 lower than 20% resulted in a reduction of PPO activity while at 80%

O2 the highest rate was obtained (30 ◦C, pH 5.5). This is in contrast with

results of Heimdal et al. (1995) where PPO activity of cut lettuce was lowest

when packed in bags with 80% O2 + 20% CO2, although the effect could have

been linked with the application of CO2 too. In our experiments, the PPO

activity decreased with high O2 concentrations over 60%. Tian et al. (2002)

also observed that PPO extracted from longan fruit peel stored at O2 concen-

trations of 70% had a lower activity than PPO from fruit peels stored at air

conditions. These authors extracted the enzyme from fruits stored during 40

days under a controlled atmosphere of 70% O2 and the PPO activity was de-

termined by measuring the product accumulation spectrophotometrically for

60 s at 25 ◦C in air. Also Kaewsuksaeng et al. (2005) reported a reduction of

browning and PPO activity and a decrease in PAL activity in longan fruit under

high oxygen atmospheres. Earlier, Barry-Ryan and O’ Beirne (1998) mentioned

that in vitro PPO activity was dramatically affected by high oxygen content.

Based on preliminary trials with a model assay enzyme system, they suggested

a non-competitive inhibition from the oxygen on the enzyme. According to our

results, high oxygen interfered with the enzyme-substrate complex to give an

inactive complex which cannot undergo further reaction to yield the product

(uncompetitive inhibition).

In addition to the inhibitive effect of high O2 concentrations, the PPO ac-

tivity was negatively affected by the reaction products present in the solution

(Figure 4.6). The products of nearly all enzyme-catalyzed reactions behave

as inhibitors when they are present in the reaction mixture. This product in-

hibition is almost always reversible (Jakubowski, 2005). When PPO is used

114 4.4 Discussion

for bioconversion of organic compounds, many of which are industrial residues

such as phenols, immobilisation of PPO on supports like nylon or capillary-

membranes was necessary. Otherwise, PPO in solution was inactivated by the

reaction products (Burton, 2001; Burton et al., 1998). According to Burton

(2001) the ortho-quinone products interact with the active site of PPO. Maz-

zafera and Robinson (2000) also mentioned that the activity of PPO extracted

from coffee decreased because of product inhibition. In this case, the main

substrate for PPO was also CGA. Also apple PPO activity was inhibited by

oxidation products of CGA (Le Bourvellec et al., 2004). It is plausible that the

inhibition is due to the structural similarity of the product and the substrate

which seems to be related to the competitive kinetics used here to explain the

product effect on the reaction rate.

The constant for product inhibition (Kmc) calculated from the combined

model was much lower than Kmu (the constant for high oxygen inhibition)

(Table 4.2), indicating that the reaction was mainly inhibited by the reaction

product and to a much lesser extent by high oxygen concentrations. B-carotene

was considered as an efficient inhibitor of apricot PPO-activity when the value of

the inhibition constant was 0.5 mM (de Rigal et al., 2000). A number of studies

have mentioned that PPO has a low affinity for its substrates and described

Km values between 1-10 mM (2.4 mM for PPO extracted from lettuce (Heimdal

et al., 1994)) although Km as low as 0.02 mM were reported for PPO from apple

(Fenoll et al., 2002). These values were always reported for monophenol and

diphenol substrates. It is more difficult to determine experimentally the kinetic

constants of the enzyme with respect to O2 due to the low value of Km as related

to oxygen (Fenoll et al., 2002). The method used in this chapter for the kinetic

data analysis is advantageous when characterisation of the enzyme with classic

methods is difficult due to low Km-values. In our study the Km values were

between 50 and 270 µM depending on the initial O2 concentration (Figure 4.4)

and as low as 3 µM when estimated using the overall model (Table 4.2). Similar

results have been reported by de Rigal et al. (2000) who observed a very high

affinity of apricot PPO towards O2 (real Km= 25 µM). Values obtained here

are also in accordance with those previously reported by (Fenoll et al., 2002)

who reported Km values related to O2 ranging from 1.3 to 38.2 µM depending

on the diphenolic compound used as substrate for mushroom PPO.

4.4.2 Brown discoloration of fresh-cut butterhead lettuce

When evaluating browning, the sensory panel could distinguish between fresh-

cut lettuce leaves stored at different gas atmospheres. When the scores were

Analysis and modelling of high oxygen effects on enzymatic brown discoloration 115

subjected to a procrustes transformation, these differences became significant

at the 95% confidence level. L∗-values measured on cut lettuce leaves did not

correspond with the browning scores. The same difficulty was encountered by

Heimdal et al. (1995) who stated that the measured L∗-values were influenced

by shadows of the inhomogeneous shredded lettuce. The mix of dark and light

green tissue parts is another source of heterogenity that hampers reproducible

colour measurements on leaves. The problem could be solved by measuring

colour on the more homogeneous tissue of lettuce stem disks. L∗-values as

measured on the stem disks were in good agreement with the browning scores

of the fresh-cut lettuce leaves, although stem disks are another tissue type with

a greater cut surface than cut leaves.

Based on the results of consecutive disk measurements over a period of time,

browning was delayed by application of elevated CO2 and high oxygen concen-

trations. Heimdal et al. (1995) previously reported on a decreased PPO activity

in shredded iceberg lettuce packed at 80% O2 and 20% CO2 but it was unclear

if this was because of the high O2 or high CO2 content inside the package.

The specific influence of both gases became more clear after modelling the

L∗-values with an autocatalytic PPO based browning model. We want to note

that, since model validation still has to be carried out, care has to be taken

with interpretation of these results. High O2 concentrations mainly affected

the parameter kbrown. kbrown described the oxidation of diphenols to quinones

and determined the slope of the decreasing part of the curve. This finding is in

agreement with the results of the in vitro PPO assay, where an inhibiting effect

on PPO activity could be attributed to high O2 concentrations. There was no

significant difference between kbrown at 5% O2 and 21% O2. Also in the in vitro

PPO assay, levels of 5% O2 were not low enough to inhibit PPO activity, due

to the very high affinity of PPO for O2. Although in the in vitro assays, PPO

activity was additionally inhibited by the reaction product, incorporating a term

describing product inhibition into the stem browning model did not significantly

improve the model fit.

High oxygen atmospheres might decrease browning in other ways too. Ap-

plication of high oxygen induces oxygen stress that would induce an antioxidant

protective system resulting in a better maintenance of membrane integrity. This

was found by Lu and Toivonen (2000) in apples that were treated with high

oxygen before slicing. Slices from treated apples browned less. Also in peaches

membrane integrity was kept longer when stored under high oxygen atmospheres

(Wang et al., 2005) as a consequence of a slower decrease in activity of the an-

tioxidant enzymes superoxide dismutase and catalase and a less rapid increase

116 4.5 Conclusions

of lipoxygenase activity. Exposure to pure oxygen significantly prevented peel

browning of litchi fruits and contributed to maintenance of membrane integrity

(Duan et al., 2004). Barry-Ryan and O’ Beirne (1998) on the other hand, found

no difference in lipoxygenase activity of cut lettuce and sliced apples, but levels

of the antioxidant ascorbic acid were slightly higher in cut lettuce stored at high

oxygen. Also Day (2001) reported on a slower decrease of antioxidant levels in

high oxygen stored fresh prepared lettuce and onions, but the antioxidant effi-

ciency against lipid peroxidation would be lower. Zheng et al. (2005) reported

that superatmospheric oxygen atmospheres improved the antioxidant activity

in strawberries and blueberries but only during the initial stage of storage.

CO2 affected ksen, the parameter describing the senescence process and de-

termining the length of the initial shoulder of the L∗ curves. Since CO2 did not

affect kbrown which described the PPO catalysed reaction, our results do not

confirm that CO2 would be a competitive inhibitor of PPO (Siriphanich and

Kader, 1985; Heimdal et al., 1995). Furthermore, results from preliminary in

vitro PPO assays showed no effect from CO2 on in vitro mushroom PPO activ-

ity (data not shown). Other researchers stated that high CO2 atmospheres are

delaying senescent browning by limiting the production of phenolic compounds

(Mateos et al., 1993; Lopez-Galvez et al., 1996).

A final remark is that sometimes it is difficult to distinguish between dete-

rioration because of microbial and enzymatic processes (Jacxsens, 2002). Since

microbial growth and mainly growth of pseudomonads (which may have a pro-

teolytic nature) is also influenced by the gas atmosphere, effects on browning

may be partly linked with effects on the microbial growth.

4.5 Conclusions

The in vitro kinetics of PPO with respect to oxygen concentrations from 5 to

100% in the presence of the reaction product have been described. The product

had an inhibiting effect on the reaction rate. The inhibiting effect of oxygen was

most clear at low product concentrations. The existence of inhibitory effects

was explained by a combined model that incorporated the product (oxidised

chlorogenic acid) and the oxygen as the inhibitors. This model was the most

appropriate to explain reaction kinetics with product as a competitive inhibitor

and oxygen as an uncompetitive inhibitor. The obtained results indicated that

superatmospheric oxygen concentrations could be considered as effective for

avoiding enzymatic browning by PPO. The methodology used here has been

shown useful to study the effect of changing conditions on enzyme activity by

Analysis and modelling of high oxygen effects on enzymatic brown discoloration 117

analysing full depletion curves.

Visual browning scores on cut lettuce leaves corresponded with L∗-values

measured on lettuce stem disks. Browning was delayed under atmospheres con-

taining elevated O2 and CO2 concentrations. The inhibitive effects were mod-

elled using an autocatalytic PPO based browning model. Whereas the effect of

high O2 concentrations was linked with a decreased PPO activity, which con-

firmed the results of the in vitro assay, CO2 mainly delayed senescence and the

herewith linked loss in membrane integrity. O2 concentrations of 5% were not

low enough to decrease browning and did not influence PPO activity.

118 4.5 Conclusions

Chapter 5

Overall MAP simulation

model

5.1 Introduction

The quest for an ideal MA package to store a given fresh(-cut) fruit or veg-

etable product can be done in different ways. The first one is a ‘pack-and-

pray’ approach based on trial-and-error (Hertog, 2003). Although this approach

still predominates in commercial practice (Peppelenbos and van ’t Leven, 1996;

Jacxsens et al., 2001), it is time-consuming and may lead to suboptimal pack-

ages. An alternative way is to gather knowledge on produce and package, which

is then combined and incorporated in models. The design is then based on com-

puter simulations. Two main model-based approaches can be distinguished. A

technique that is often applied is the steady state approach. In this approach

the models are used to calculate the gas conditions that would occur at steady

state conditions, as was done e.g. in Jacxsens (2002). The disadvantage of this

method is that the dynamic phase before reaching the steady state is neglected.

However, knowledge on the equilibrium gas conditions is of great importance

in low O2 MA packages, where the purpose is to reach the desired steady state

conditions as quickly as possible. In case the respiration of a produce is too

slow to rapidly reach steady state conditions in the package, active packaging

can be applied. In this application, packages are flushed with the desired gas

mixture prior to sealing.

High O2 MA packaging on the other hand, always requires active packaging.

A high initial O2 level will be accomplished inside the package by flushing and

should last as long as possible in order to maximise the benefits from the high

119

120 5.2 Development of a MAP simulation model

O2 conditions. Anoxic situations, which can occur after an initial high O2

period in packages with a low permeable film, should be avoided. CO2 ideally

reaches elevated steady state levels as quickly as possible, in order to profit from

beneficial CO2 levels as long as possible. The design of such packages requires

the use of dynamic models. This is of importance to predict how long the desired

high O2 levels can be kept inside the packages.

In order to predict gas- and quality changes inside high O2 MA packages of

strawberry and fresh-cut butterhead lettuce, a set of models that are dynamic

in time will be incorporated in a MAP simulation model. Model equations that

describe respiration, microbial growth and brown discoloration as previously

developed in respectively Chapter 2, 3 and 4 will be incorporated in the MAP

simulation model. In this chapter the development (Section 5.2) and applica-

tions (Section 5.3) of the MAP simulation model will be described.

5.2 Development of a MAP simulation model

5.2.1 Model equations

In order to develop a MAP simulation model, a set of ODE’s (ordinary differen-

tial equations) based on the model equations for respiration, microbial growth

and brown discoloration as previously developed in respectively Chapter 2, 3

and 4 was programmed in MATLAB®. In the following subsections, the ODE’s

as they will be used in the MAP simulation model will be described.

5.2.1.1 Mass balances for O2 and CO2

The concentrations of O2 and CO2 inside a package can change due to respira-

tion of the produce (O2 consumption (rO2,cons) and CO2 production (rCO2,prod))

and diffusion of the gases through the package film. The change in O2 and CO2

molar masses (nO2 , nCO2) inside a package is given by:

dnO2

dt= rO2,diff −MrO2,cons (5.1)

dnCO2

dt= MrCO2,prod − rCO2,diff (5.2)

The terms rO2,diff and rCO2,diff (mol s−1) describe diffusion of respectively

O2 and CO2 through the package film, defined as in equation (5.3) and (5.4).

rO2,diff =DO2A

d(pO2,∞ − pO2) (5.3)

Overall MAP simulation model 121

rCO2,diff = −DCO2A

d(pCO2,∞ − pCO2) (5.4)

DO2 and DCO2 are the diffusion coefficients of the package film for O2 and

CO2 (mol s−1 Pa−1 m−1), A is the area of the package film (m2) and d is the

thickness of the package film (m). pO2,∞ and pCO2,∞ are the O2 and CO2 partial

pressure (Pa) of the atmosphere surrounding the package. pO2 and pCO2 are the

O2 and CO2 partial pressures (Pa) inside the package. The diffusion coefficients

are temperature dependent and can be described according to Arrhenius:

D(C)O2 = D(C)O2ref exp(−EaD(C)O2

R

(1T− 1

Tref,diff

))(5.5)

Where D(C)O2ref is the reference diffusion coefficient for oxygen or carbon

dioxide, Tref,diff is the reference temperature for diffusion (K), T is the tem-

perature (K), EaD(C)O2is the activation energy (J mol−1), R is the ideal gas

constant (8.31 J mol−1 K−1).

rO2,cons (mol s−1) describes the consumption rate of O2 due to respiration

(equation (5.6)) and rCO2,prod (mol s−1) describes the production rate of CO2

due to respiration (equation (5.7)). The Arrhenius equation was used to describe

the temperature dependence of the maximum respiration rates for O2 and CO2

(VmO2 and VmCO2) (equation (5.8)). These are the model equations previously

described as model #2 in Chapter 2 (equation (2.11)-(2.15)) with CO2 and

high O2 as noncompetitive inhibitors of respiration and with VmO2 and VmCO2

dependent on the temperature.

rO2,cons = VmO2

pO2

(KmO2 + pO2) (1 + pCO2/KmnCO2)1

1 + pO2/KmO2i(5.6)

with VmO2 the maximum oxygen consumption rate (mol kg−1 s−1), KmO2

and KmnCO2 (Pa) the Michaelis-Menten constant for O2 and noncompetitive in-

hibition of CO2, respectively. KmO2i (Pa) is the parameter describing inhibition

of high oxygen concentrations.

rCO2,prod = rqoxrO2 + VmCO2

11 + pO2/KmO2f

(5.7)

rqox is the respiratory quotient, VmCO2 is the maximum respiration rate for

CO2 (mol kg−1 s−1), KmO2f (Pa) is the parameter describing the inhibition on

the fermentative metabolism by O2.

Vm(C)O2 = Vm(C)O2ref exp(−EaVm(C)O2

R

(1T− 1

Tref,resp

))(5.8)

122 5.2 Development of a MAP simulation model

Vm(C)O2ref (mol kg−1 s−1) is the maximum respiration rate of O2 or CO2

at the reference temperature Tref,resp (278 K (5 ◦C) for fresh-cut butterhead

lettuce and 283 K (10 ◦C) for strawberry) and EaVm(C)O2is the activation energy

(J mol−1).

In order to express the changes in O2 and CO2 concentration as partial

pressures instead of molar masses, equations (5.1) and (5.2) were written as

follows:

dpO2

dt=

RT

Vfree(rO2,diff −MrO2,cons) (5.9)

dpCO2

dt=

RT

Vfree(MrCO2,prod − rCO2,diff) (5.10)

where the term RTVfree

is applied to express the change of O2 and CO2 as partial

pressures using the ideal gas law with Vfree the free volume of the package (m3)

calculated as follows:

Vfree = V − M

ρ(5.11)

V is the total volume of the package (m3), ρ is the density of the packed

product (kg m−3), the term Mρ defines the volume in the package occupied by

the product. M is the mass of the product (kg). Vfree and T are considered

time-independent.

The values for the respiration model parameters are shown in Table 5.1.

As can be seen from equation (5.3) and (5.4), gas diffusion through the film

is driven by gas concentration differences inside and outside the package. In the

case of passive packaging, the initial gas concentrations inside the package are

equal to atmospheric conditions. As soon as CO2 is produced inside the package

due to respiration, CO2 will migrate from the inside to outside the package. O2

inside the package will be consumed. As a consequence O2 will migrate into

the package. When active packaging is applied, the gas atmosphere inside the

package is modified by flushing with an appropriate gas mixture prior to sealing

the film. When flushing with a gas mixture with high O2 concentrations, the

O2 concentration in the package will decrease due to respiration and initially

a migration of O2 outside the package due to a negative O2 concentration gra-

dient. As soon as the O2 concentration inside the package falls below 21 kPa

(atmospheric level), the concentration gradient turns positive and O2 migration

will occur from outside to inside the package.

The respiration model is valid for O2 concentrations between 0 and 100 kPa

and CO2 concentrations between 0 and 20 kPa (strawberry) and between 0

Overall MAP simulation model 123

Table 5.1: Model parameter values for respiration model #2 (as developed in Chapter

2) for fresh-cut butterhead lettuce and strawberry.

Model parameter Lettuce Strawberry

VmO2ref (mol kg−1 s−1) 96×10−9 242×10−9

EaVmO2 (J mol−1) 85×103 64×103

VmCO2ref (mol kg−1 s−1) 50×10−9 175×10−9

EaVmCO2 (J mol−1) 24×103 65×103

rqox (-) 0.77 0.66

KmO2 (Pa) 1.0×103 1.2×103

KmnCO2 (Pa) 42×103 53×103

KmO2i (Pa) 1008×103 Not significant (set to + ∞)

KmO2f (Pa) 2.2×103 0.14×103

and 10 kPa (fresh-cut lettuce). The valid temperature range for the strawberry

model is 2 to 14 ◦C and for fresh-cut lettuce 1 to 9 ◦C.

5.2.1.2 Dynamic predictive bacterial growth models

In order to describe the microbial growth as a function of changing environmen-

tal conditions (in our case changing gas conditions inside a package), a dynamic

microbial growth model must be applied. The analytical solution of the Baranyi

model as applied in Chapter 3 (equations (3.1)-(3.3)) can only be applied when

the environmental conditions are constant. The reason is that the lag time is not

interpretable if the environmental conditions change during growth. Since the

lag period is a process of adjustment of the bacterial cells to the post-inoculation

environment, the lag period can only be described by a single parameter when

this environment is constant.

When describing the cell growth under dynamic environmental conditions,

the following pair of ordinary differential equations can be used (Baranyi and

Roberts, 1994):

dn

dt=

11 + e−Q(t)

µmax(E(t))(1− en−nmax) (5.12)

dQ

dt= µmax(E(t)) (5.13)

with initial conditions n(0)=n0 and Q(0)=Q0=ln(q0). µmax(E(t)) describes

µmax as a function of the environmental conditions changing over time (E(t)).

n(t) is the natural logarithm of the cell numbers (ln cfu.cm−2), Q(t) is the

124 5.2 Development of a MAP simulation model

natural logarithm of the physiological state of the cells q(t) and nmax is the

maximum population density (ln cfu.cm−2).

As can be seen from equations (5.12) and (5.13), n(t) is affected by the initial

bacterial cell concentration, n0 as well as by the physiological state of the inocu-

lum, q0. The physiological state of the cells at inoculation is determined by their

previous growth history and by the new environment at inoculation. Until now,

there is no easy way to determine q0 experimentally (Baranyi et al., 1995). How-

ever, q0 can be calculated from data obtained from experiments under constant

environmental conditions. Baranyi et al. (1995) found that values of the phys-

iological state derived from static experiments enabled accurate predictions of

Brochothrix thermosphacta growth under a range of varying temperature condi-

tions. The calculation of q0 is based on Baranyi and Roberts (1994) who proved

that for cultures having identical physiological states at inoculation and being

cultivated under constant environmental conditions, the product of the lag time

and the maximum specific growth rate will be a transformed version of q0.

λ · µmax = h0 = ln(

1 +1q0

)(5.14)

Hence, the value of q0 can be calculated as:

q0 =1

exp(h0)− 1(5.15)

In the Pseudomonas fluorescens overall model #2 (see Chapter 3), λ was

defined as (see also equation (3.6)):

λ =lm

µmax(5.16)

Based on equation (5.14) and (5.16) it can be seen that the value of lm

provides an estimation of the parameter h0 from which q0 can be calculated.

For lm equal to 1.6 (see Table 3.4), q0 equals 0.25.

As in equation (3.4), µmax will be defined as:

õmax = m + mO2pO2(t) + mCO2pCO2(t) (5.17)

where pO2(t) and pCO2(t) are the O2 and CO2 partial pressures (Pa) in the

package changing as a function of time.

Note that the static P. fluorescens overall model #2 (as described in equa-

tions (3.1)-(3.3), (3.4) and (3.6)) and the dynamic model (described in equations

(5.12), (5.13) and (5.17)) are two different forms of the same model. As a con-

sequence, the previously reported parameter estimates are still valid. They are

given in Table 5.2. Note that, compared to the originally reported estimates,

Overall MAP simulation model 125

Table 5.2: Parameter estimates for the dynamic Pseudomonas fluorescens growth

model.

Parameter Estimates

n0 (ln cfu cm−2) 8.2

nmax (ln cfu cm−2) 19.7

q0 (-) 0.25

m (s−1/2) 0.0001

mO2 (s−1/2 Pa−1) -2.4 × 10−10

mCO2 (s−1/2 Pa−1) -1.7 × 10−9

some units changed to be consistent with the units applied in other equations

of the MAP model. The static form of the model can only predict growth under

constant gas conditions. The dynamic form is considered to be able to describe

growth at changing O2 and CO2 concentrations, which is based on the hypoth-

esis that λ × µmax is constant during the storage period. However, Bernaerts

et al. (2002) and Swinnen et al. (2006) showed that sudden temperature shifts

induced intermediate lag phases in the growth profile of Escherichia coli K12.

In these cases, the product of λ and µmax was not a constant, but its evolution

could be linked to the amplitude of the temperature shift (Swinnen et al., 2006).

With relation to our application, it is assumed that changes in gas condition

inside an MA package are gradual and that λ × µmax can be assumed to be

constant.

In the overall model that was suggested to describe the growth of Listeria

innocua in Chapter 3, λ as well as µmax were described as a function of CO2.

Since in dynamic models there is no independent parameter for the lag, the

previously suggested Listeria innocua model can not be written dynamically

as could be done for Pseudomonas fluorescens overall model #2. In order to

provide an estimation for q0, the following static model was fitted to the L.

innocua data set for model identification (i.e. constant gas conditions): the

static Baranyi-equation (equation (3.1)-(3.3)), with µmax defined as in equation

(3.7):

µmax = m(CO2max − CO2) (5.18)

and λ defined as

λ =l

µmax(5.19)

A weighted least squares optimisation procedure was carried out using the

126 5.2 Development of a MAP simulation model

Table 5.3: Parameter estimates and corresponding approximate 95% confidence in-

tervals for the ‘new’ static Listeria innocua growth model.

Parameter Estimates ± 95% confidence limits

n0 (log cfu cm−2) 3.75 ± 0.01

nmax (log cfu cm−2) 6.82 ± 0.01

l (-) 0.014 ± 0.111

m (h−1/2) 0.002 ± 0.00004

CO2max (h−1/2 %−1) 30.39 ± 0.12

Table 5.4: Parameter estimates for the dynamic Listeria innocua growth model.

Parameter Estimates

n0 (ln cfu cm−2) 8.6

nmax (ln cfu cm−2) 15.7

q0 (-) 71

m (s−1/2) 7.2

CO2max (s−1/2 Pa−1) 8.4 × 10−6

MATLAB® least squares non-linear optimisation routine (lsqnonlin) with the

Levenberg-Marquardt method as was explained in Section 3.2.5 of Chapter 3 to

estimate the parameters. The parameter estimates are given in Table 5.3.

The predictive power of this model is comparable to that of the previously

suggested model for L. innocua based on the adjusted R2 (0.91 and 0.92 respec-

tively) and RMSE (0.35 and 0.34 respectively).

q0 can now be calculated based on the estimate for l, analogously as was done

for P. fluorescens. The dynamic model to describe the growth of L. innocua and

to be implemented in the MAP simulation model is then defined by equations

(5.12), (5.13) and

µmax = m(CO2max − pCO2(t)) (5.20)

The parameter estimates of this model are given in Table 5.4. The same

unit changes as for the dynamic P. fluorescens model were applied.

The microbial growth models are valid at 7 ◦C and CO2 concentrations be-

tween 0 and 25 kPa. Although the models were originally built on growth data

at O2 concentrations between 20 and 100 kPa, they will be used in the MAP

model for O2 concentrations between 0 and 100 kPa. For the L. innocua model

it is justified to apply the model as such at low O2 concentrations since growth

of this bacterium is only influenced by the CO2 concentration. This statement

Overall MAP simulation model 127

is based on the results of our in vitro experiments and in vivo validation exper-

iments (see Chapter 3) and literature results (Bennik et al., 1995). Also the P.

fluorescens model was predicting growth on lettuce stored at 5 kPa O2 fairly

well (see Chapter 3). However, in this model, increasing O2 concentrations re-

sult in a decrease in µmax, which only holds for oxygen concentrations between

20 and 100 kPa. Indeed, applying the model as such to O2 levels below 20 kPa

would imply faster predicted growth due to increased growth rates at low O2

concentrations. According to Bennik et al. (1998), growth of P. fluorescens was

the same at 1 and 21 kPa O2. Consequently, in our MAP simulation model, P.

fluorescens growth at low oxygen concentrations below 20 kPa will be predicted

as growth at 20 kPa.

5.2.1.3 Dynamic brown discoloration models

To describe brown discoloration of fresh-cut lettuce inside a package, the auto-

catalytic PPO based browning model developed on lettuce stem disk L∗-values

(see Chapter 4) was applied, the O2 and CO2 concentration now being time-

dependent:

d[PPO]/dt = kPPO[P ][FA]

d[P ]/dt = −kPPO[P ][FA]

d[Q]/dt = k′brown

11 + pO2(t)/KiO2

[PPO][DP ]

d[DP ]/dt = −k′brown

11 + pO2(t)/KiO2

[PPO][DP ] (5.21)

d[FA]/dt = k′sen

11 + pCO2(t)/KiCO2

[FR]

d[FR]/dt = k′sen

11 + pCO2(t)/KiCO2

[FR]

L∗ = L∗0 − (L∗0 − L∗+∞

[DP ]0)[Q] (5.22)

with the initial values (at t=0) defined as follows:

128 5.2 Development of a MAP simulation model

Table 5.5: Parameter values describing browning of lettuce to be used in the MAP

simulation model.

Parameter Value

kPPO (s−1) 1.2 × 10−5

k′brown (s−1) 3.5 × 10−7

KiO2 (Pa) 149 × 103

k′sen (s−1) 1 × 10−4

KiCO2 (Pa) 4 × 103

FR0 0.0001

L∗+∞ 40

[PPO] = PPO0 = 0

[P ] = P0 = 1

[Q] = Q0 = 0 (5.23)

[DP ] = DP0 = 0.001M

[FA] = FA0 = 0

[FR] = FR0

The parameters to be used in this model are those estimated for the auto-

catalytic PPO browning model fitted per stem disk (see also Table 4.4). The

estimates units were adapted to be consistent with the other models and are

given in Table 5.5.

A prediction of the L∗-values of stem disk only becomes helpful in predicting

storability of fresh-cut lettuce if L∗-values can be correlated with (un)acceptable

levels of the brown discoloration. Previously, in Section 4.3.2.1, a relation was

made between browning scores as judged by a sensory panel on the one hand

and measured L∗-values of lettuce stem disks on the other hand (see Figure

4.11). Since L∗-values before storage are variable among batches of lettuce (as

can also be seen from the difference in L∗0-values between the experiment pre-

sented in Section 4.3.2.1 (L∗0=63 ± 0.7) and 4.3.2.2 (L∗0=55 ± 0.7)), relative

L∗-values rather than absolute values should be used to define a correlation be-

tween L∗-values and sensory browning scores. Relative L∗-values were obtained

by dividing the absolute values by the average L∗0-value of the experiment pre-

sented in Section 4.3.2.1 (being 63). In Figure 5.1 the relation between browning

scores and relative L∗-values is given. The line represents the correlation be-

tween both parameters as calculated using the PLS procedure of SAS/STAT®.

Overall MAP simulation model 129

1 2 3 4 50.75

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

Browning score

Rel

ativ

e L*

−va

lue

Figure 5.1: Correlation between relative L∗-values and browning scores.

Since a browning score of 3 can be considered as the limit between unaccept-

able (browning score higher than 3) and acceptable (browning score lower than

3) lettuce, the correlated cut-off relative L∗-value is 0.92. In other words, a

decrease of more than 8% of the L∗-value will be considered as unacceptable.

The brown discoloration model is valid between 5 and 100% oxygen; 0 and

75% carbon dioxide and at a temperature of 7 ◦C.

5.2.2 Development of a user interface

Equations (5.9), (5.10), (5.12), (5.13) and (5.21) were solved together as a set

of ordinary differential equations in MATLAB® using ODE solver ODE45.

In Table 5.6 an overview of the parameters of the MAP simulation model

is given. A distinction is made between parameters that can be adapted by

the user (user-defined parameters) and pre-defined parameters which can not

be altered by the user.

130 5.2 Development of a MAP simulation model

Table 5.6: Model parameters of the MAP simulation model.

Model parameters related to Parameter values

Pre-defined values User-defined values

General M

V

R

T

Diffusion DO2ref

(film dependent) DCO2ref

A

d

EaDO2

EaDCO2

Tref,diff

pO2,∞

pCO2,∞

pO2,i

pCO2,i

Respiration KmO2

(Product dependent) KmnCO2

KmO2i

KmO2f

rqox

VmO2ref

VmCO2ref

EaVmO2

EaVmCO2

Tref,resp

Microbial growth

Pseudomonas fluorescens n0

continued on next page

Overall MAP simulation model 131

continued from previous page

Linked to Parameters

Pre-defined values User-defined values

q0

Nmax

m

mO2

mCO2

Listeria innocua

n0

q0

Nmax

m

CO2max

Brown discoloration L∗0

PPO0

P0

Q0

L∗+∞

FA0

FR0

kPPO

k′brown

k′sen

KiO2

KiCO2

A user interface was created using GUIDE, the MATLAB® Graphical User

Interface development environment and examples are shown in Figure 5.2 and

5.3.

The user interface allows the user to choose values for the user-defined pa-

rameters (see Table 5.6) in the units most currently used for the respective

parameters and to make choices on the product or package film using pop-up

menus. On the left side of the window, the input parameters are classified in 5

different groups:

• Package-related (in grey): choice of package film (pop-up menu), V (ml

or cm3), A (cm2), d (µm), pO2,i (kPa) and pCO2,i (kPa)

132 5.2 Development of a MAP simulation model

• Product-related (in green): choice of product: strawberry or fresh-cut

butterhead lettuce (pop-up menu), M (kg)

• Environmental conditions (in blue): T ( ◦C)

• Microbiology-related (in yellow): n0 for Pseudomonaceae and L. innocua

(log cfu g−1)

• Brown discoloration-related (in orange): L∗0 of lettuce stem disks

The diffusivity for oxygen and carbon dioxide of the included films in the

package pop-up menu are given in Table 5.7. Since information on temperature

dependency of the O2- and CO2-permeabilities were not given, values for EaDO2

and EaDCO2were taken equal to 47000 and 35000 J mol−1. These values were

chosen according to values published for polypropylene by Langowski (1996).

Application of the first three films was reported by Ragaert (2005), who

used the EMAP (Equilibrium Modified Atmosphere Package) film for straw-

berry and lettuce to pack respectively ‘Elsanta’ strawberries and mixed lettuce

at an equilibrium gas atmosphere of 3% O2 and 5% CO2. In Ragaert (2005),

the high-barrier film was perforated to pack the produce at air conditions. In

our application, the permeabilities of the high-barrier film without perforations

are used, in order to simulate the evolution of gases and quality when a produce

is packed in a low-permeable film. The next three films were applied by Del No-

bile et al. (2006) for the packaging of minimally processed Iceberg and Romaine

lettuce. The biodegradable film is a mixture of three biodegradable polyesters.

The other two films are high gas permeability polyolefinic films, commercially

used to package minimally processed products. Additionally, the program al-

lows to choose a ‘virtual’ package film for strawberry and lettuce. With this

tool, the user can ‘design’ new package films by adapting O2 and CO2 diffu-

sivities and simulating the behaviour of these films on gas and quality changes

inside the package. Although using this tool, O2 and CO2 diffusivities and ac-

tivation energies for diffusivity can be chosen freely, some practical information

on package films should be taken into account. Two main groups of films can

be distinguished: the so-called ‘breathable’ films and micro-perforated films.

‘Breathable films’ are usually blends of two or three different polymers. Films

can also be laminated to convey properties that are not possible with a sin-

gle film (e.g. a laminate of oriented polypropylene (oPP) to polyethylene (PE)

will have the stiffness and clarity of oPP but with the good seal performance

of PE). Gas transport through these films occurs only through gas diffusion.

The permeability of these films reaches maximally about 4000 to 5000 ml O2

Overall MAP simulation model 133

Table 5.7: Oxygen and carbon dioxide diffusivities (mol s−1 Pa−1 m−1) and Tref,diff

( ◦C) of package films included in the MAP simulation program. EaDO2and EaDCO2

were set at 47000 and 35000 J mol−1 respectively, for all films.

Package film DO2ref DCO2ref Tref,diff

High-barrier (Ragaert, 2005) 1 × 10−18 1 × 10−18 23

EMAP film lettuce (Ragaert, 2005) 3.5 × 10−16 3.1 × 10−15 7

EMAP film strawberry (Ragaert, 2005) 5.4 × 10−16 3.1 × 10−15 7

Biodegradable (Del Nobile et al., 2006) 1.2 × 10−16 1.4 × 10−15 5

Polyolefinic 1 (Del Nobile et al., 2006) 3.1 × 10−16 1.5 × 10−15 5

Polyolefinic 2 (Del Nobile et al., 2006) 2.1 × 10−16 8.1 × 10−16 5

Lettuce virtual film User-defined User-defined 7

Strawberry virtual film User-defined User-defined 7

m−2 d−1 atm−1. For ‘breathable’ films the O2 : CO2 diffusivity ratio is 1:3

to 1:4. Micro-perforated films have higher permeability rates and gas transport

mainly occurs through the minuscule perforations in the film. The O2 : CO2

diffusivity ratio is 1:1. Information on film permeability provided by package

film companies is often given at 23 ◦C and usually in units of ml O2 m−2 d−1

atm−1. Unfortunately, information on temperature dependence is hardly avail-

able. Generally it can be stated that activation energy for permeability is low

compared to that of respiration, but still it might influence the permeability

considerably.

After pressing the button ‘Start simulation’, the program starts to calculate.

The program then returns graphical and numerical outputs. The graphical

output consists of three graphs: in the first graph the O2 (full line) and CO2

(dashed line) concentrations (kPa) in the package are represented as a function of

time (days), the second graph represents the growth curves of Pseudomonaceae

(open circles) and L. innocua (closed circles) as a function of time (days) and in

the third graph the evolution of the L∗-value is given as a function of time (days).

Results of subsequent simulations are given in different colours: simulation 1, 2,

3, 4 and 5 in respectively blue, green, red, lighter blue and pink. This feature can

be applied to investigate the influence of changing parameters on the output.

The numerical output of the program is given in a table on the right side of

the window and consists of 5 rows each containing the output of one simulation.

The details of the input parameters of an executed simulation can be written by

the user in the first column. In the second column, the time (hours) is given at

which the CO2 concentration inside the package reaches too high values (>10

kPa for fresh-cut butterhead lettuce and >20 kPa for strawberry). The time

134 5.2 Development of a MAP simulation model

Figure 5.2: Example of the use of the MAP simulation program on strawberry pack-

ages.

(hours) when oxygen levels reach levels below 0.5 kPa (as an indication for

anaerobic conditions), is given in the third column. In the fourth column the

time (hours) at which spoilage of lettuce occurs (i.e. when the concentration of

Pseudomonads is above 8 log cfu g−1) is given. The time (hours) at which the

concentration of Listeria innocua reaches levels above 2 log cfu g−1, used as a

model for hazardous levels of Listeria monocytogenes, is represented in column

five. In the sixth column, the time (hours) is given at which fresh-cut butterhead

lettuce becomes unacceptable due to enzymatic brown discoloration, defined by

a 8% decrease in L∗-value as explained previously.

As an example for the use of the Graphical user interface, some simulations

of strawberry and fresh-cut butterhead lettuce MA packages are shown in Figure

5.2 and 5.3. In Figure 5.2 the results of three consecutive simulations of 500 g

strawberries packed in a high-barrier film (110 µm thick, 858 cm2 area disposable

for gas exchange, total volume of 1500 ml) are shown.

For the first simulation a temperature of 2 ◦C, and air (21 kPa O2, 0 kPa

CO2) as initial gas condition inside the package was chosen. In the upper graph

the change in O2 and CO2 for the first simulation is given in blue. As could

be expected, the concentration of O2 rapidly decreased in the high-barrier film,

whereas the concentration of CO2 rapidly increased. O2 levels fell below 0.5

Overall MAP simulation model 135

Figure 5.3: Example of the use of the MAP simulation program on cut lettuce

packages.

kPa after 61 hours, as can be read in the output table at the left of the window

(from the column ‘Anaerobic’). The nick in the CO2 concentration curve is a

consequence of enhanced CO2 production due to the onset of a fermentative

metabolism around these very low O2 levels. The CO2 levels then exceeded the

limit of 20 kPa very rapidly. In the output table, the time at which this critical

limit was crossed (in this case after 80 hours) is given in the column ‘High CO2’.

In this way the user knows from which time point on the output of the program

must be interpreted with care.

The second simulation (green) shows what happens when the same straw-

berry package is put at a higher temperature of 7 ◦C. The decrease in O2 and

increase in CO2 content of the package was faster than at 2 ◦C, resulting in

anaerobic O2 levels after 36 hours and CO2 levels above 20 kPa after 47 hours.

In the third simulation (red), the package was also held at 7 ◦C but the initial

O2 concentration was changed to 100 kPa. At these conditions it took 217 hours

to reach anaerobic conditions, but CO2 levels exceeded 20 kPa after 45 hours.

In Figure 5.3 the results of four consecutive simulations of 250 g cut but-

terhead lettuce packed in a high-barrier film (110 µm thick, 1100 cm2 area

disposable for gas exchange, total volume of 2000 ml) are shown. In the first

simulation setup (blue, 2 ◦C and air as initial gas condition), it took more than

136 5.2 Development of a MAP simulation model

240 hours (>10 days) to reach anaerobic conditions but levels above 10 kPa

CO2 were reached after 180 hours. Since the temperature was 2 ◦C, the first

simulation only generates output related to the gas conditions inside the pack-

age since microbial growth and brown discoloration models are only valid at

7 ◦C. If the same package was held at 7 ◦C (second simulation, green), res-

piration of the lettuce increased with as a consequence a more rapid decrease

of O2 (anaerobic conditions after 196 hours) and a rapid increase in CO2 lev-

els. Spoilage and pathogen levels became problematic after 128 and 89 hours

respectively. As previously explained these numbers are linked to predicted

growth of Pseudomonads and Listeria innocua which are shown in the second

graph. The third graph represented the evolution of the L∗-value as a measure

for enzymatic brown discoloration. Unacceptable browning levels were reached

within 124 hours. Care must be taken with the interpretation of the number

of hours at which unacceptable browning occurs since it occurs after CO2 lev-

els exceeded 10 kPa (96 hours), the limit for validity of the lettuce respiration

model. In red the results of a third simulation are shown. Here, the initial O2

concentration was set at 100 kPa. In this case, the O2 concentration did not

reach anaerobic levels before 240 h. The increase in the CO2 concentration was

somewhat slower than for simulation 2 because of the small inhibitory effect of

high O2 concentrations on the respiration of the cut lettuce. This somewhat

slower CO2 increase resulted in a slightly faster growth of L. innocua. The dif-

ferential response of the two studied bacteria is nicely illustrated. The predicted

Pseudomonads growth drastically retarded due to the high O2 concentrations,

whereas changes in O2 levels did not affect L. innocua. The effect on browning

can be seen as a reduction of the slope, which is also a high O2 effect. In light

blue the results of simulation 4 are given, where an initial O2 and CO2 concen-

tration of respectively 95 and 5 kPa were considered. Due to the higher initial

CO2 levels, the predicted growth of both micro-organisms retarded as compared

to simulation 3. The retarding effect on brown discoloration can be seen as an

increase in initial shoulder of the L∗-value curve.

We are aware that in the above shown simulations the validity range of the

MAP simulation model was often exceeded. However, our prior intention of

these simulations was to explain how the program can be used and no further

conclusions will be drawn from these ‘instructive’ simulations.

Overall MAP simulation model 137

5.3 Application of the MAP simulation model

The MAP simulation program can be used for several purposes. First of all,

in terms of optimal package design, the MAP model provides an alternative

for the time-consuming ‘pack-and-pray’ approach, that still predominates in

commercial practice (Hertog, 2003). Two different approaches to design optimal

high oxygen MA packages for strawberry and fresh-cut lettuce are discussed

in Section 5.3.1. Another application of the MAP simulation program is a

sensitivity study which is the subject of Section 5.3.2. The sensitivity study

will provide information on the impact of model parameter uncertainty on the

model predictions.

5.3.1 Design of packages

The MAP simulation program provides a simulation-based alternative to a trial-

and-error approach for the design of packages. Before starting to simulate, a

definition of what is a suitable package must be found. Ideally, this definition

is not only based on requirements of the (dynamic) gas conditions inside the

package, but also on quality criteria simultanously predicted using quality mod-

els. In the current version of the MAP simulation program, quality models are

only included for fresh-cut butterhead lettuce. Quality models for strawberry

are not yet available. For strawberry, the search for an optimal MA package

will therefore only be based on the predicted gas atmospheres inside the package

whereas the search for an optimal fresh-cut lettuce MA package will addition-

ally be based on the predicted microbial growth and predicted enzymatic brown

discoloration. As soon as quality models for strawberry will become available,

these should be incorporated in the simulation program.

To optimise a package, different degrees of freedom of the MAP model may

be adapted. Two main strategies can be followed to search for an optimal MA

pack. When a specific package film is at one’s disposal, package characteristics

(volume (V ), area of the film (A), packaged product mass (M), initial O2 and

CO2 concentrations (pO2,i and pCO2,i)) can be altered in order to find an op-

timal package. The other way around when package dimensions are set, film

characteristics may be adapted. Both strategies can be applied using the MAP

simulation program. For the first strategy, the graphical interface allows the

user to choose the package film of interest through the film pop-up menu and to

adapt V , A, M , pO2,i and pCO2,i. For the second strategy, the user can choose

for the ‘virtual’ films in the film pop-up menu and adapt the O2 and CO2 dif-

fusivities (DO2ref and DCO2ref ) in the MATLAB® program while keeping the

138 5.3 Application of the MAP simulation model

other package characteristics constant.

5.3.1.1 Strawberry high O2 MA package

As previously mentioned, the search for an optimal strawberry MA package will

be purely based on the gas composition inside the package throughout the stor-

age period. A first question that arises in this case is: ‘What are the optimum

target gas conditions for a strawberry package?’. To answer this question, re-

sults of preliminary trials on the effect of high O2 concentrations on strawberry

aroma and data available from literature on optimal low O2 MA conditions

on strawberry quality were considered. According to studies performed in our

laboratory, strawberries stored at 100 kPa were appreciated most by a sensory

panel according to color and aroma. CO2 is known to increase strawberry firm-

ness (Harker et al., 2000; Smith and Skog, 1992) and to reduce fungal growth

(Perez and Sanz, 2001). However, CO2 levels as high as 20 kPa were the cause

of off-flavour production (Berna et al., 2007). Van der Steen et al. (2002) sug-

gested CO2 levels between 5 and 10 kPa combined with O2 levels between 3 and

5 kPa as optimal low O2 MA equilibrium conditions for strawberry packages.

Summarising this information, an optimal high O2 MA package for strawberry

should maintain a high O2 concentration as long as possible in combination with

an elevated CO2 concentration which should not exceed 10 kPa. Maintaining

high O2 concentrations inside a package is only possible when a film with low

oxygen permeability is used. However, when a fast respiring produce as straw-

berry is packaged in a low O2 permeable film, O2 levels might soon drop to

anaerobic levels especially under circumstances of temperature abuse (see e.g.

the results of the third simulation in Figure 5.2). The challenge that one faces

when searching for an ideal high O2 MA package is to meet two requisites, which

in se are opposite in terms of O2 permeability requirements: provide a high O2

concentration as long as possible without danger to create anoxic situations in

case of temperature abuse. The simulations shown in this Section will make this

point more clear.

In a first set of simulations, two existing films, namely the high-barrier film

and EMAP film for strawberry were used. As previously explained, dimensions

of the package (M , V and A) may then be changed for better or for worse. Re-

alistic strawberry package dimensions, as determined from strawberry packages

in the supermarket, were taken as start values for these parameters (M=500 g,

V =1500 ml, A=858 cm2). The volume of the package was determined by sub-

merging the package in a fixed volume of water and then measuring the increase

in water volume. The result of a simulation of a strawberry package with an

Overall MAP simulation model 139

0 2 4 6 8 100

10

20

30

40

50

60

70

80

90

100

Time (days)

Con

cent

ratio

n O

2, CO

2 (kP

a)

Figure 5.4: Gas concentrations as a function of time in a strawberry high O2 MA

package using the strawberry EMAP film (V=1500 ml, A=858 cm2). O2 concentra-

tions are given by the full lines, CO2 concentrations are given by the dashed lines. In

blue and green, a simulation at 7 ◦C with a film thickness of respectively, 30 and 60

µm is given. In red, a temperature of 14 ◦C and a film thickness of 60 µm is simulated.

initial O2 concentration of 100 kPa in a high-barrier film at 7 ◦C can be seen

from simulation three (red) in Figure 5.2. While high O2 levels were kept rela-

tively long, anoxic conditions were reached within 217 hours. Also CO2 reached

levels above 10 kPa very quickly. At more severe temperature abuse condi-

tions (e.g. 14 ◦C), these avoidable conditions were reached even more quickly:

anoxic levels after 106 hours and CO2 levels above 10 kPa after 11 h. To avoid

these unappropriate conditions, ‘breathable’ films should be used. When the gas

permeabilities of such films meet the respiration characteristics of the packed

produce, appropriate equilibrium gas conditions can be established. The EMAP

film (see Table 5.7) for strawberry was used by Ragaert (2005) for low O2 MA

packaging of strawberry. In Figure 5.4 simulations for high O2 MA packaging

of strawberry with this EMAP film are shown.

A film thickness of 30 µm, according to Ragaert (2005), was chosen initially.

M , V and A were initially set equal to 500 g, 1500 ml and 858 cm2 respectively,

according to realistic package dimensions. The result of the first simulation

is given in blue (Figure 5.4). In this package, high O2 atmospheres were not

maintained for a long period. After less than three days, the O2 levels dropped

below 20 kPa. In contrast to the high-barrier package, the gas conditions in the

EMAP film package evolved to equilibrium conditions. The equilibrium O2 and

140 5.3 Application of the MAP simulation model

0 2 4 6 8 100

10

20

30

40

50

60

70

80

90

100

Time (days)

Con

cent

ratio

n O

2, CO

2 (kP

a)

Figure 5.5: Strawberry high O2 MA package using a virtual film with DO2ref equal

to 1 × 10−15 mol s−1 Pa−1 m−1 and DCO2ref equal to 2 × 10−15 mol s−1 Pa−1 m−1

at 2 ◦C, 7 ◦C and 14 ◦C in blue, green and red respectively.

CO2 level were 0.9 kPa and 5.5 kPa, respectively. However, strawberry quality

would benefit from higher CO2 levels. By doubling the film thickness (to 60

µm in the second simulation represented in green) the equilibrium levels are 0.4

kPa for O2 and 10 kPa for CO2, and O2 levels were kept above 20 kPa a little

longer due to the lower permeability at greater film thickness. At 14 ◦C (third

simulation, red), equilibrium levels of 0.3 kPa O2 and 13 kPa CO2 were realised.

It is clear that with the given films, we could not find a suitable high O2

package for strawberry. In the next step we therefore will search for optimal gas

permeabilities (or diffusivities defined as DO2ref and DCO2ref ) of a film to pack

strawberries under high O2 atmospheres. The same package dimensions as used

in the first strategy are used here. As a result of various simulations (with d=30

µm, M=500 g, V =1500 ml and A=858 cm2, results not shown), it was found

that with a film with DO2ref equal to 1 × 10−15 mol s−1 Pa−1 m−1, equilibrium

O2 conditions of 4.3, 3.0 and 1.9 kPa were obtained at respectively 2, 7 and

14 ◦C. CO2 equilibrium conditions were 6.2, 8.0 and 10 kPa at respectively 2,

7 and 14 ◦C if DCO2ref was equal to 2 × 10−15 mol s−1 Pa−1 m−1. The results

of these simulations are given in Figure 5.5.

From the above shown simulations it becomes clear that for fast respiring

products as strawberries, it is impossible to find suitable high O2 MA films. In

films that avoid unappropriate equilibrium conditions as anoxic conditions and

too high CO2 levels, superatmospheric O2 conditions are kept for only a short

Overall MAP simulation model 141

period. The positive effect of high O2 atmospheres would therefore be limited.

Packages that would keep superatmospheric O2 conditions for a longer period

are more likely to develop too high levels of CO2 and/or too low levels of O2.

5.3.1.2 Fresh-cut butterhead lettuce high O2 MA package

Other than for strawberry, the search for an optimal cut lettuce high O2 MA

package can be founded on microbial quality and enzymatic brown discoloration

of the lettuce. Additionally, CO2 levels should not exceed 10 kPa in order to

prevent damage to the lettuce and O2 levels should not get too low in order to

prevent anaerobic fermentation. Standard package dimensions as determined

from cut lettuce packages from the supermarket (V =2000 ml, A=1100 cm2,

M=250 g) were chosen initially.

First, simulations were done at 7 ◦C using different films found in literature as

suitable for lettuce (see Figure 5.6). The characteristics of these films are given

in Table 5.7. In packages with the high-barrier film (blue), the CO2 concentra-

tion reached values above 10 kPa after 104 hours. Therefore this film was con-

sidered as unsuitable. Equilibrium CO2 levels of 6.1, 4.6, 4.3 and 2.4 kPa were

obtained in packages with polyolefinic 2 film (pink), biodegradable film (red),

polyolefinic 1 film (light blue) and lettuce EMAP film (green), respectively.

High O2 levels were kept considerably longer in the package with biodegradable

film than with the polyolefinic 1 and 2 film and lettuce EMAP film. Quality

as based on all three defined quality criteria (spoilage, pathogenic growth and

brown discoloration) was kept longest in the package with the biodegradable

film and polyolefinic 2 film.

It is clear that the biodegradable film as well as the polyolefinic 2 film,

were quite suitable for high O2 MA packaging of fresh-cut butterhead lettuce.

The search for a new package film, given the package dimensions as previously

applied, could therefore be based on the permeability characteristics of these

films. With a CO2 permeability of 8.1 × 10−16 mol s−1 Pa−1 m−1 of the

polyolefinic 2 the equilibrium CO2 conditions were 3.8 and 7.1 kPa CO2 at

respectively 1 and 9 ◦C. Given the O2 permeability of the biodegradable film,

high O2 concentrations were maintained in the package for a long period of

time. Lowering the O2 permeability would result in a package where the high

O2 concentrations are kept for an even longer time.

Consequently, simulations were carried out using information on existing

films having similar characteristics as the optimal ‘virtual’ film. Oxygen trans-

mission rates (OTR’s) of different films were kindly provided by two package

film companies: Compagnie Franco Suisse SAS (France) and Amcor Flexibles

142 5.3 Application of the MAP simulation model

0 2 4 6 8 100

10

20

30

40

50

60

70

80

90

100

Time (days)

Con

cent

ratio

n O

2, CO

2 (kP

a)

a

0 2 4 6 8 100

1

2

3

4

5

6

7

8

9

Time (days)

Pse

udom

onas

(o)

− L

iste

ria(.

)

b

0 2 4 6 8 1045

50

55

60

Time (days)

L*−

valu

e

c

Figure 5.6: Cut lettuce high O2 MA package (V=2000 ml, A=1100 cm2, M=250 g)

using different films: high barrier film (blue), lettuce EMAP film (green), biodegrad-

able film (red), polyolefinic 1 film (light blue), polyolefinic 2 film (pink). In figure a,

the gas concentration in the packages is given (O2 in full lines, CO2 in dashed lines). In

figure b, the number of Pseudomonads and Listeria are given as log cfu.cm−2. Figure

c represents the decrease in L∗-value over time.

Overall MAP simulation model 143

(The Netherlands). Simulations were carried out with different films with OTR’s

ranging from 25 to 2000 ml O2 m−2 d−1 atm−1 at 23 ◦C. Taking into account

the thickness of each film (which ranged from 30 to 50 µm), these values corre-

sponded with O2 diffusivities between 4 × 10−16 and 4 × 10−18 mol s−1 Pa−1

m−1. According to the companies information, CO2 transmission rates were

taken equal to 4 times the corresponding OTR. Since no information was avail-

able on temperature dependence of the film permeabilities, the same values as

assumed before as based on literature results (EaDO2and EaDCO2

were set at

47000 and 35000 J mol−1 respectively, see Table 5.7) were used. The follow-

ing package dimensions were used in the simulations: V =1300 ml, A=968 cm2,

M=100 g, with initial O2 and CO2 concentrations equal to 95 kPa and 5 kPa

respectively. All films provided appropriate CO2 equilibrium conditions with O2

levels decreasing to between 50 and 80 kPa after 10 days. In all cases, quality

was kept longer than in a simulated low O2 MA package. The most promising

results were obtained for the film with a low OTR of 25 ml O2 m−2 d−1 atm−1.

5.3.2 Sensitivity analysis

The MAP simulation model is based on average package dimensions and aver-

age characteristics on film, product respiration, microbial growth and enzymatic

brown discoloration. In practical situations however, variations on these average

characteristics do occur. Before applying the model in more practical situations,

it is interesting to investigate how ‘sensitive’ the model response is to parameter

fluctuations. In sensitivity analyses, the impact of model parameter fluctuations

on the model output was investigated. Using our MAP simulation model, simu-

lations with parameters within a range of values could easily be carried out. The

effect of variations of 20% of the parameter were investigated using simulations.

The values of the overall MAP model parameters as given in Tables 5.1, 5.2, 5.4

and 5.5 are used as ‘default’ values.

The results of a sensitivity analysis strongly depend on the MA package

under study (Hertog, 2003). For example, parameters related to fermentative

respiration will only be of importance in packages where anaerobic conditions

occur, variations in activation energies will not have an influence in packages held

at a temperature equal to the reference temperature. Because of the importance

of the package on the sensitivity analysis, two different packages per product

were considered in the sensitivity analyses. The packages were chosen based on

the results of the previous Section and can be considered as suitable high O2

MA packages.

144 5.3 Application of the MAP simulation model

5.3.2.1 Strawberry high O2 MA package

Sensitivity analysis for two high O2 MA packages for strawberry were carried

out at 7 ◦C. Hereto, the effect of a 20% variation in the model parameters on

the following 5 output criteria was investigated:

1. the time at which the O2 concentration gets below 60 kPa (hours)

2. the time at which the CO2 equilibrium concentration is reached (hours)

3. the CO2 equilibrium concentration (kPa)

4. the time at which the O2 equilibrium concentration is reached (hours)

5. the O2 equilibrium concentration (kPa)

The following two strawberry packages with the following characteristics were

considered:

• Package 1: V =1500 ml, A=858 cm2, ‘Virtual film for strawberry’ with

DO2ref=1 × 10−15 mol m−1 s−1 Pa−1, DCO2ref=2 × 10−15 mol m−1 s−1

Pa−1, EaDO2=47000 J mol−1, EaDCO2

=35000 J mol−1, Tref,diff=7 ◦C.

• Package 2: V =1695 ml, A=600 cm2, ‘EMAP film for strawberry’ with

DO2ref=5.4 × 10−16 mol m−1 s−1 Pa−1, DCO2ref=3.1 × 10−15 mol m−1

s−1 Pa−1, EaDO2=47000 J mol−1, EaDCO2

=35000 J mol−1, Tref,diff=7 ◦C.

film thickness (30 µm), temperature (7 ◦C), product mass (500 g) and ini-

tial gas condition (100 kPa O2) were equal for both packages. These pack-

ages resulted from the search for a suitable strawberry high O2 MA package

as presented in section 5.3.1.1, where for package 1 and 2 respectively the gas

permeabilities of the film and the package dimensions were adapted.

The packages with the above mentioned characteristics will further be called

the ‘default’ packages. First, simulations with these ‘default’ packages were car-

ried out. The numerical output criteria of these simulations serve as a reference

value in the sensitivity analyses. Consequently, simulations were carried out

changing input parameters with 20% (parameter + 20% and parameter - 20%)

one by one keeping the other model parameters constant. In Figure 5.7 an ex-

ample of simulations is given, using the first package configuration and changing

volume and area. The output criteria of these simulations were compared to

these of the simulations with the ‘default’ packages and were expressed as per-

centage decrease (negative percentages) or increase (positive percentages) of the

output criteria. In Table 5.8 the results of the sensitivity analysis based on the

Overall MAP simulation model 145

0 2 4 6 8 100

10

20

30

40

50

60

70

80

90

100

Time (days)

Con

cent

ratio

n O

2, CO

2 (kP

a)

Figure 5.7: Example of simulations with varying volume and area for sensitivity

analysis of the first strawberry package. Full lines and dashed lines represent O2 and

CO2 concentrations respectively for a ‘Default’ package (blue), volume -20% (green),

volume +20% (red), area -20% (light blue), area +20% (pink).

two different strawberry packages are shown. The first column mentions which

parameter was changed. In the other columns, the percentual change in output

criteria (with numbers as given in the list at the beginning of this section) is

given for the two packages separated with a slash (/).Table 5.8: Sensitivity analysis for two strawberry high O2 MA packages. Percentual

changes as related to two ‘default’ packages are given for 5 output criteria (1. time

at which the O2 concentration gets below 60 kPa (hours), 2. time at which the

CO2 equilibrium concentration is reached (hours), 3. CO2 equilibrium concentration

(kPa), 4. time at which the O2 equilibrium concentration is reached (hours), 5. O2

equilibrium concentration) (kPa). Results for package 1 and package 2 are separated

by /.

parameters 1 2 3 4 5

default 0 0 0 0 0

V -20% -33/-28 -23/-20 2/2 -30/-26 -5/-5

V +20% 33/33 21/20 -2/-3 28/25 6/5

A-20% 17/17 21/27 21/23 3/4 -33/-36

A+20% -11/-11 -14/-13 -15/-16 -4/-6 41/41

continued on next page

146 5.3 Application of the MAP simulation model

continued from previous page

parameters 1 2 3 4 5

d-20% -11/-11 -16/-20 -18/-19 -6/-9 53/51

d+20% 11/17 19/20 17/19 3/4 -28/-31

pO2,i-20% -44/-44 0/0 -1/0 -7/-6 0/0

M -20% 22/17 2/0 -20/-22 25/19 62/58

M+20% -17/-11 -5/0 19/21 -23/-20 -32/-34

T -20% 11/17 5/7 -7/-8 12/12 10/11

T+20% -11/-11 -5/0 7/8 -12/-11 -9/-10

DO2ref -20% 17/17 2/0 0/0 0/3 -37/-37

DO2ref+20% -11/-11 0/0 0/0 -3/-6 48/42

DCO2ref -20% 0/0 21/27 20/23 2/1 7/2

DCO2ref+20% 0/0 -14/-13 -14/-16 -2/0 -4/-1

EaDO2-20% 0/0 0/0 0/0 0/0 0/0

EaDO2+20% 0/0 0/0 0/0 0/0 0/0

EaDCO2-20% 0/0 0/0 0/0 0/0 0/0

EaDCO2+20% 0/0 0/0 0/0 0/0 0/0

KmO2-20% 0/0 2/0 1/0 -1/1 -13/-10

KmO2+20% 0/0 0/0 -1/0 0/0 12/9

KmnCO2-20% 0/0 -2/0 -3/-1 3/1 6/2

KmnCO2+20% 0/0 2/7 2/1 -2/0 -4/-1

KmO2f -20% 0/0 0/0 0/0 0/0 0/0

KmO2f+20% 0/0 0/0 0/0 0/0 0/0

rqox-20% 0/0 -5/-7 -19/-21 -3/-1 -5/-2

rqox+20% 0/0 5/7 18/21 2/1 5/2

VmO2ref -20% 6/6 -5/-7 -19/-21 14/10 58/55

VmO2ref+20% -6/-6 2/7 18/21 -14/-12 -30/-33

VmCO2ref -20% 0/0 0/0 0/0 0/0 0/0

VmCO2ref+20% 0/0 0/0 0/0 0/0 0/0

EaVmO2-20% 0/0 2/7 6/6 -5/-4 -12/-12

EaVmO2+20% 0/6 0/0 -5/-6 4/3 14/14

EaVmCO2-20% 0/0 0/0 0/0 0/0 0/0

EaVmCO2+20% 0/0 0/0 0/0 0/0 0/0

From Table 5.8 it can be seen that general trends in the sensitivity anal-

ysis are similar for the two strawberry packages. Changes to the parameters

EaDO2and EaDCO2

did not influence the model response at all, since the sim-

ulation temperature (7 ◦C) was equal to the reference temperature for diffusion

(Tref,diff). Also the influence of changes to the fermentation-linked parameters

Overall MAP simulation model 147

KmO2f , VmCO2ref and EaVmCO2was nihil, since the two considered strawberry

packages were designed to avoid fermentation favouring gas conditions.

As for the respiration-linked parameters, rqox and VmO2ref were the most

important. Where the effect of VmO2ref was on both the equilibrium conditions

and times to reach these conditions, rqox mainly influenced the CO2 equilibrium

level. Respiration-linked parameters with a more modest influence were KmO2 ,

EaVmO2and KmnCO2 .

The initial O2 partial pressure was logically of great importance for the time

at which O2 levels fall below 60 kPa and to a lesser extent on the time to reach

equilibrium O2 conditions. Since the parameter pCO2,icould not be changed

without changing the initial O2 level in the current package setup (100 kPa),

it was not possible to take into account changes to pCO2,i. Other simulations

(results not shown) however, showed that the value of this parameter mainly

influenced the time to reach equilibrium CO2 conditions. DO2ref and DCO2ref

were of importance for respectively O2 and CO2 related output criteria.

The parameters T , M , A and d had an important influence on all consid-

ered output criteria, whereas V mainly influenced times to reach equilibrium

conditions and O2 levels below 60 kPa.

In general, package characteristics were of greater importance than respiration-

linked parameters.

5.3.2.2 Fresh-cut butterhead lettuce high O2 MA package

A sensitivity analysis was also carried out for a fresh-cut butterhead lettuce

high O2 MA package with the following characteristics: M=500 g, V =2000 ml,

A=1100 cm2, ‘Virtual film for lettuce’ with DO2ref = 1.10−16 mol m−1 s−1

Pa−1, DCO2ref = 1.10−15 mol m−1 s−1 Pa−1, EaDO2= 47000 J mol−1, EaDCO2

= 35000 J mol−1, Tref,diff = 7 ◦C, d=30 µm, T=7 ◦C. This package reaches

an equilibrium CO2 concentration of around 5.5 kPa after 96 h and the O2

concentration is 49 kPa at the end of the simulation period (240 h). Values for

n0 for Pseudomonads, and Listeria and L∗0-value, were defined as 3.5 log cfu

g−1, 0.001 log cfu g−1 and 56, respectively.

For the fresh-cut butterhead lettuce package also quality models were con-

sidered in the sensitivity analysis, the output criteria being the following:

1. the time at which the O2 concentration gets below 80 kPa

2. the time at which the CO2 equilibrium concentration is reached (hours)

3. the CO2 equilibrium concentration (kPa)

148 5.3 Application of the MAP simulation model

4. the time at which microbial spoilage occurs (hours) i.e. when concentra-

tion of Pseudomonads is above 8 log cfu cm−2

5. the time at which dangerous levels of Listeria occur (hours) i.e. when

concentration of Listeria is above 2 log cfu cm−2

6. the time at which unacceptable browning occurs (hours) defined by a 8%

decrease in L∗-value

The results are shown in Table 5.9. For the same reasons as for the straw-

berry packages, the parameters EaDO2, EaDCO2

, KmO2f , VmCO2ref and EaVmCO2

did not influence the model output. Also in accordance to the results for straw-

berry, rqox and VmO2ref were the most important respiration-linked model pa-

rameters with both having a comparable influence on the quality criteria with

the influence in decreasing order for spoilage, pathogenic growth and browning.

The influence of changes to other parameters for respiration was negligible.

Since in the quality models no term was incorporated to describe a temper-

ature effect and the models were only valid at 7 ◦C, the effect of changes in T

on the quality criteria for spoilage, pathogenic growth and browning could not

be simulated.

Decreasing the initial O2 concentration from 100 kPa to 80 kPa, had a great

effect on spoilage growth (12% faster) and brown discoloration (6% faster) but

did not influence the growth of Listeria due to its oxygen insensitive nature. As

for strawberry, increasing pCO2,i mainly influenced the time to reach equilibrium

conditions. Since with the given ‘default’ package pCO2,icould not be changed

without altering O2i some additional simulations were carried out where pO2,i

was equal to 80 kPa, and pCO2,i was set at 0, 5 and 10 kPa (Figure 5.8). In

the ‘default’ package (with no added CO2), the equilibrium condition was only

reached after 156 hours. As can be seen from Figure 5.8, increasing pCO2,idras-

tically retards microbial spoilage, pathogenic growth and enzymatic browning.

Changes in the package parameters A, M , V and d had an influence on the

considered output criteria linked with gas concentrations (criteria 1 to 3).

The influence of changing parameters on browning was rather small, except

for pO2,i. Listeria growth, as only influenced by CO2, was mainly influenced by

changes in parameters affecting the (time to reach) equilibrium CO2 conditions,

more specifically: M , V , rqox and VmO2ref . Spoilage was influenced by the

following parameters in decreasing order of importance: pO2i, rqox, M , VmO2ref ,

d, A, V , DO2ref , DCO2ref , KmO2i and EaVmCO2 .

Overall MAP simulation model 149

Table 5.9: Sensitivity analysis for a fresh-cut butterhead lettuce high O2 MA package.

Percentual changes as related to the ‘default’ package are given for 6 output criteria

(1. time at which the O2 concentration gets below 80 kPa (hours), 2. time at which

the CO2 equilibrium concentration is reached (hours), 3. CO2 equilibrium concen-

tration (kPa), 4. time at which microbial spoilage occurs (hours), 5. time at which

dangerous levels of Listeria occur (hours), 6. time at which unacceptable browning

occurs (hours)).

parameters 1 2 3 4 5 6

default 0 0 0 0 0 0

V -20% -23 -4 12 1 2 0

V +20% 24 -1 -12 -1 -2 0

A-20% 9 21 20 3 0 1

A+20% -7 -14 -14 -3 -1 -1

d-20% -7 -17 -17 -3 -1 -1

d+20% 7 17 16 2 0 1

pO2,i -20% -100 2 2.4 -12 0 -6

M -20% 19 -19 -30 -4 -4 -1

M+20% -14 14 28 4 4 1

T -20% 18 -9 -18 nd* nd* nd*

T+20% -14 7 19 nd* nd* nd*

DO2ref -20% 8 0 0 1 0 0

DO2ref+20% -6 0 0 -1 0 -1

DCO2ref -20% 1 21 20 2 0 0

DCO2ref+20% 0 -14 -14 -2 -1 0

KmO2-20% 0 1 0 0 0 0

KmO2+20% 1 0 0 0 0 0

KmnCO2-20% 2 -4 -4 -1 0 0

KmnCO2+20% -1 3 3 0 0 0

KmO2i-20% 2 -1 -2 -1 0 0

KmO2i+20% 4 -3 -6 -1 -1 0

KmO2f -20% 0 0 0 0 0 0

KmO2f+20% 0 1 1 0 0 0

rqox-20% -1 -17 -27 -5 -2 -1

rqox+20% 2 14 26 5 2 1

continued on next page

150 5.4 Conclusions

continued from previous page

parameters 1 2 3 4 5 6

VmO2ref -20% 15 -18 -27 -4 -2 -1

VmO2ref+20% -11 14 26 3 2 1

VmCO2ref -20% 0 0 0 0 0 0

VmCO2ref+20% 0 1 1 0 0 0

EaVmO2-20% 4 -4 -7 -1 -1 0

EaVmO2+20% -3 4 7 1 0 0

EaVmCO2-20% 0 0 0 0 0 0

EaVmCO2+20% 0 0 0 0 0 0

nd*: no data since out of model validity range

5.4 Conclusions

A MAP simulation model which can be used to design high O2 MA packages

for strawberry and fresh-cut butterhead lettuce was developed in MATLAB®.

A graphical user interface allows the user to easily view the effect of chang-

ing package characteristics on the package gas conditions and product quality

during the storage period through graphical and numerical output. Until now,

quality models, more specifically for microbial growth and enzymatic brown

discoloration are only included for fresh-cut butterhead lettuce. As soon as

strawberry quality models are available, they should be incorporated in the

model in order to improve the power of the simulation program, since eventu-

ally, quality and safety are the major factors to which should be optimised since

they are directly related to the shelf life.

Using the MAP simulation program, a sensitivity analysis of the model pa-

rameters could easily be carried out, performing consecutive simulations varying

the parameters one by one. Although results of sensitivity analysis strongly de-

pend from package to package, some important trends became apparent in the

analyses on both strawberry and fresh-cut butterhead lettuce packages. Most

important respiration-linked parameters are the maximum O2 consumption rate

(VmO2) and the respiratory quotient (rqox). The importance of the package-

related characteristics M (product mass), d (film thickness), V (total pack-

age volume) and A (film area) is comparable or even greater than that of the

respiration-linked parameters.

The MAP simulation program was also applied to search for ideal high O2

MA packages for strawberry and fresh-cut butterhead lettuce. In general, an

ideal high O2 MA package should keep a high O2 concentration as long as

Overall MAP simulation model 151

0 2 4 6 8 100

10

20

30

40

50

60

70

80

90

100

Time (days)

Con

cent

ratio

n O

2, CO

2 (kP

a)

a

0 2 4 6 8 100

1

2

3

4

5

6

7

8

9

Time (days)

Pse

udom

onas

(o)

− L

iste

ria(.

)

b

0 2 4 6 8 1045

50

55

60

Time (days)

L*−

valu

e

c

Figure 5.8: Simulations with varying initial CO2 levels for sensitivity analysis of a

fresh-cut butterhead lettuce MA package (V=2000 ml, A=1100 cm2, M=250 g, initial

O2 concentration=80 kPa and initial CO2 concentration=0 kPa (blue), 5 kPa (green)

or 10 kPa (red)). The change in gas concentrations, microbial numbers and L∗-value

as a function of time is given in figure a, b and c respectively.

152 5.4 Conclusions

possible. CO2 levels should rapidly reach suitable (elevated) equilibrium levels.

These requisites should be accomplished without danger for anaerobic and/or

too high CO2 conditions. Based on our simulations, high O2 MAP showed to

be unsuitable for the storage of strawberries, mainly due to their fast respiring

nature. In packages that avoid unappropriate equilibrium conditions (such as

anoxic conditions and too high CO2 levels), superatmospheric O2 conditions are

kept for only a short period. The positive effect of high O2 atmospheres would

therefore be limited in these packages. The application of high O2 MA packages

for fresh-cut butterhead lettuce looked more promising. This was mainly due

to the lower respiration rate of fresh-cut butterhead lettuce, which allows to

pack in a film with limited O2 permeability to maintain high O2 concentrations

in the package for a long period of time without danger of falling into anoxic

situations within the period of storage. Packages can be designed in order to

reach a CO2 equilibrium level around 10 kPa. For 100 g of fresh-cut butterhead

lettuce packaged in a film with a film area of 968 cm2 and a total volume of

1300 ml under an initial atmosphere of 95 kPa O2 and 5 kPa CO2, films with an

OTR between 25 and 2000 ml O2 m−2 d−1 atm−1 (and CO2 transmission rate

= 4 times the OTR) were found to significantly improve microbial and visual

quality of the produce as compared to a low O2 MA package.

Chapter 6

General conclusions and

future work

6.1 General conclusions

On top of stringent temperature control, modified atmosphere packaging (MAP)

can be used to keep the quality and suppress the growth of undesired micro-

organisms on a wide variety of products, including raw and cooked meats, fish,

fresh pasta, fruit and vegetables. To reduce respiration and the associated qual-

ity loss of vegetable and fruit produce, typically low levels of oxygen in combina-

tion with elevated carbon dioxide concentrations are applied. The application of

superatmospheric oxygen concentrations has been proposed as an alternative to

low O2 MAP for fresh fruits and vegetables and would be particularly effective

for fresh-cut vegetables that are sensitive to enzymatic browning and spoilage

by yeasts (Day, 1996; Jacxsens et al., 2001; Gomez et al., 2006).

The main objective of this thesis was to develop a model-based methodology

for the design of high O2 MA packages for strawberry and fresh-cut butterhead

lettuce.

Since gas conditions inside an MA package are the result of respiration of

the produce on the one hand and diffusion of the gases through the package

film on the other hand, knowledge and modelling of respiration rates is essential

for the design of MA packages. Respiration measurements of fresh-cut but-

terhead lettuce and strawberry are scarce especially under superatmospheric

oxygen concentrations. Furthermore, no models are available that include res-

piration under superatmospheric oxygen atmospheres until now. Therefore, the

effects of oxygen (ranging from 0 to 100 kPa), carbon dioxide (ranging from 0

153

154 6.1 General conclusions

to 20 kPa) and temperature (ranging from 1 to 9 ◦C for fresh-cut butterhead

lettuce and from 2 to 14 ◦C for strawberry) on the oxygen consumption and car-

bon dioxide production of ‘Zendria’ fresh-cut butterhead lettuce and ‘Elsanta’

strawberries, were evaluated. Respiration of strawberries stored at superatmo-

spheric oxygen concentrations was comparable to respiration at atmospheric

oxygen levels. On the other hand, high oxygen levels had an inhibitive effect on

the respiration rate of fresh-cut butterhead lettuce, the inhibitive effect being

more pronounced at higher temperatures. Low temperatures, elevated carbon

dioxide concentrations and oxygen concentrations below 2 kPa significantly re-

duced the respiration rate of both products. Carbon dioxide concentrations of

10 kPa inhibited the respiration of fresh-cut butterhead lettuce, whereas respi-

ration rates at 20 kPa CO2 were comparable to respiration rates at 0 kPa CO2,

probably due to an injury response. For strawberry, carbon dioxide concentra-

tions up to 20 kPa inhibited the respiration rate. The strong reducing effect

of low O2 concentrations on the respiration was according to Michaelis-Menten

kinetics. Since the respiration of strawberry at superatmospheric O2 concen-

trations was comparable to that at air conditions, no adaptations were made

to the original Michaelis-Menten equation with respect to O2 dependency. To

describe the inhibitive effect of high O2 levels on the respiration rate of fresh-cut

butterhead lettuce a noncompetitive high oxygen inhibition term was included.

This adapted equation succesfully described the inhibitive effect due to the ap-

plication of high O2. In order to describe the respiration of both products over

the range of temperatures and carbon dioxide concentrations, temperature in-

fluences on the maximum specific respiration rate of oxygen and carbon dioxide

were described according to Arrhenius and the inhibitive effect of carbon diox-

ide was described by a noncompetitive inhibition term. The constructed overall

models described the respiration data of strawberry and fresh-cut butterhead

lettuce well as indicated by the high R2adj values of 0.96 and 0.82, respectively.

Next, we investigated how microbial quality was affected by atmospheres

containing high O2 and CO2 levels. Hereto, the growth of two micro-organisms

of importance in fresh-cut butterhead lettuce was investigated on nutrient agar

plates at 7 ◦C: Pseudomonas fluorescens, a typical vegetable spoilage organism

and Listeria innocua, a model organism for the foodborn pathogen Listeria

monocytogenes. The in vitro growth of L. innocua was not affected by the

O2 concentration, whereas increased carbon dioxide levels caused a longer lag

phase and decreased maximum specific growth rate of the bacteria. The in

vitro growth of P. fluorescens was inhibited by both elevated CO2 and high

O2 concentrations. Predictive growth models that succesfully described the

General conclusions and future work 155

in vitro bacterial growth as a function of time, oxygen and carbon dioxide

concentration were obtained by incorporating the reported gas effects on lag

time and maximum specific growth rate in a Baranyi-model. The growth models

were validated by inoculating fresh-cut butterhead lettuce with L. innocua or

a GFP-tagged P. fluorescens strain. The growth of an inoculated GFP-tagged

strain of P. fluorescens on fresh-cut butterhead lettuce was very similar to what

was predicted with the model. Also the growth of the pseudomonads that

naturally contaminated the lettuce agreed well with the model prediction. In

contrast, the behaviour of L. innocua on fresh-cut lettuce was not as predicted.

Growth was considerably slower than in vitro especially at conditions without

CO2. Instead of exerting a growth retarding effect as in vitro, CO2 had a

growth promoting effect on L. innocua when studied on fresh-cut lettuce. It was

suggested that at conditions without carbon dioxide, L. innocua stood in direct

nutritional competition with the prominent group of gram-negative bacteria,

mainly pseudomonads leading to a slow growth. On the other hand, at 15% CO2

the growth of gram-negative bacteria was retarded with as a consequence a more

rapid growth of L. innocua which was more related to the model predictions.

The favorisation of L. innocua in high oxygen atmospheres in combination with

elevated CO2 may imply a potential health risk, but not to a larger extent than

for low O2 modified atmospheres.

Of concern for fresh-cut produce is the rapid enzymatic browning due to the

mechanical damage to the tissue. Since decreased browning of the cut surface

of various fresh-cut products when stored at high O2 levels was observed (Day,

1996; Barry-Ryan and O’ Beirne, 1998; Jacxsens, 2002) and polyphenoloxidase

(PPO) plays a key role in enzymatic browning in various living organisms, the

relation between high O2 concentrations and in vitro PPO activity was assessed

using small bioreactors. In a small bioreactor where a range of initial O2 levels

were set by bubbling the cell with gas mixtures containing 5 to 100 kPa O2, PPO

was brought together with its substrate chlorogenic acid. The decrease in oxygen

concentration in the bioreactor was used as a measure for the enzyme activity.

Full depletion curves were adequately described by a Michaelis-Menten model.

However, O2 concentrations initially set in the bioreactor had an influence on

the maximum reaction rate Vmax and the Michaelis-Menten constant Km, which

were respectively decreasing and increasing with increasing O2 concentrations.

These effects could be explained by a combined inhibitive effect from the high

initial O2 levels and the increased reaction product concentrations in the biore-

actor. The inhibiting effect of high O2 was most obvious at low product con-

centrations. The existence of inhibitory effects was explained by a combined

156 6.1 General conclusions

Michaelis-Menten model that incorporated the product (oxidised chlorogenic

acid) as a competitive inhibitor and the oxygen as an uncompetitive inhibitor.

The obtained results indicated that the application of superatmospheric oxygen

concentrations could be used to reduce PPO-mediated browning. When applied

to fresh-cut butterhead lettuce, high O2 and CO2 gas atmospheres indeed de-

layed browning. Visual browning scores of a sensory panel corresponded well

with L∗-values measured on lettuce stem disks. Decreases in L∗-values over

time could be modelled succesfully using an autocatalytic PPO based browning

model, that describes browning as a PPO-mediated enzymatic reaction includ-

ing the activation of an inactive precursor of PPO through the release of fatty

acids from membranes, which occurs during senescence. Whereas the inhibitive

effect of high O2 concentrations on browning was linked with a decreased PPO

activity, which confirmed the results of the in vitro assay, CO2 mainly delayed

the loss in membrane integrity.

In order to predict gas- and quality changes inside high O2 MA packages

of strawberry and fresh-cut butterhead lettuce, the above defined set of model

equations describing respiration, microbial growth and enzymatic brown dis-

coloration as a function of time was incorporated in a MAP simulation model.

The MAP simulation model was developed in MATLAB®. The graphical user

interface allows the user to easily view the effect of changing package charac-

teristics on the package gas conditions and product quality during the storage

period through graphical and numerical output. The advantage of this simula-

tion model is that packages can be optimised based on their effect on product

quality, instead of only on gas conditions inside the package. For fresh-cut but-

terhead lettuce, quality models describing brown discoloration and describing

the growth of an important group of spoilage organisms, the pseudomonads

and of L. innocua as a model organism for the pathogen L. monocytogenes, are

incorporated. Strawberry quality models should be included as soon as they

become available.

The MAP simulation model was applied to investigate how sensitive the

model response was on model variable changes in a so-called sensitivity analysis

of the model. The variables with the largest influence on the model response

were the package-related characteristics mass, thickness, volume and area. The

influence of these package characteristics was comparable or even greater than

that of respiration-linked variables. Of the latter group, the variables VmO2

(maximum O2 consumption rate) and rqox (respiratory quotient) were the most

important. Changes to other model variables were not or only minorly influenc-

ing the model response within the relevant variables values.

General conclusions and future work 157

The second application of the MAP simulation model was the optimisation

of high O2 MA packages for strawberry and fresh-cut butterhead lettuce. Sim-

ulations on strawberry packages revealed that it was hard to find a suitable

film to succesfully pack strawberries at high O2 atmospheres. In order to avoid

unappropriate conditions as anoxic conditions and too high CO2 levels, film

permeabilities should be quite high due to the fast respiration of strawberries

and high produce loading in strawberry packages. As a result, the high O2

conditions - initially set by flushing the package - and the herewith linked posi-

tive effects on strawberry quality would only be kept for a short period of time.

The application of high O2 MA packages for fresh-cut butterhead lettuce looked

more promising. Because of the lower respiration rate of fresh-cut butterhead

lettuce, it was possible to create a package where high O2 concentrations were

maintained for a long period of time without danger to fall to anoxic situations

within the storage period. Additionally, CO2 levels between 5 and 10 kPa could

be achieved, which is beneficial in terms of keeping quality of the cut lettuce.

6.2 Future work

In future work related to this thesis, the following aspects could be adressed:

• As a validation of the MAP simulation model, case studies will be carried

out in the framework of a project at the Flanders Centre of Postharvest

Technology. Produce will be packed in different films, supplied in various

package dimensions by film companies. Gas composition, microbial quality

and brown discoloration will be monitored. The obtained data will be

compared with the simulated results of the MAP model.

• The inhibitive effect of high O2 levels on the respiration of fresh-cut butter-

head lettuce appeared to be more important at higher temperatures. This

temperature-dependency could be incorporated in the respiration model

as it would improve the model predictions.

• In the MAP simulation model, only respiration can be simulated as a

function of temperature. In order to obtain a model which is fully dynamic

in time and temperature, temperature dependency should be also included

in the quality models.

• Incorporation of quality models for strawberry in the MAP simulation

model would improve its power, since eventually, quality is the major

factor to which should be optimised. The limiting factor for strawberry

158 6.2 Future work

quality is the growth of Botrytis cinerea, an important spoilage fungus of

strawberry. Other quality parameters of importance are aroma and colour

of the strawberries. Because the beneficial effect of high O2 MA packaging

for strawberry is limited due to its high respiration rate, optimisations

should be focused on controlled atmosphere (CA) conditions.

• The reduction of enzymatic brown discoloration at superatmospheric O2

levels might involve processes other than the inhibition of PPO alone.

Literature data suggest that the application of high O2 levels induces

oxygen stress that would induce an antioxidant protective system resulting

in a better maintenance of membrane integrity. This hypothesis could

be tested on fresh-cut butterhead lettuce by examining the membrane

integrity and on antioxidant capacity of the produce after the application

of high O2 stress to whole lettuce heads. More insight in the dynamics of

the reactive oxygen species (ROS) defense mechanism could be achieved

by monitoring the system superoxide anion - superoxide dismutase, the

anion being an important ROS which is neutralised by the superoxide

dismutase enzyme. In this way, more insight could be achieved in the

relation between high O2 stress and brown discoloration.

• The MAP simulation model could be extended for other products. Addi-

tionally, predictive models of micro-organisms other that Pseudomonaceae

and Listeria could be incorporated.

• The acquired information regarding microbiological responses to high oxy-

gen atmospheres could be coupled with packages for predictive microbiol-

ogy such as Growth Predictor developed at the Institute for Food Research

(Norwich, UK) or Pathogen Modeling Program developed by USDA-ARS

Eastern Regional Research Centre (Wyndmoor, Pennsylvania, USA).

Appendix 1

This appendix provides insight on the enzymatic background of equation 4.10,

that describes the rate of the PPO enzymatic reaction in the presence of a com-

petitive inhibitor (being the reaction product) and an uncompetitive inhibitor

(being O2). In essence, this model is an adapted version of the linear mixed inhi-

bition model. A short review on the linear mixed inhibition model according to

Marangoni (2003) will be given here, followed by a discussion on the adaptions

made to come to equation 4.10.

Linear mixed inhibition is a reversible type of inhibition, where the inhibitor

can interact with the free enzyme (competitive inhibition) and the enzyme-

substrate complex at a site other than the active site (uncompetitive inhibition),

as represented schematically as follows:

E + S Km←→ ES kcat−→ E + P

+ +

I I

l Ki l δKi

EI + S δKm←→ ESI

The rate equation is given by

v = kcat[ES] (6.1)

with v the rate of the reaction and kcat the rate constant for the breakdown

of ES complex to free product and free enzyme.

The dissociation constants are given by:

Km =[E][S][ES]

δKm =[EI][S][ESI]

Ki =[E][I][EI]

δKi =[ES][I][ESI]

(6.2)

The enzyme mass balance of this reaction is as follows:

159

160

[ET] = [E] + [ES] + [EI] + [ESI]

= [E] +[E][S]Km

+[E][I]Ki

+[E][S][I]KmδKi

(6.3)

The rate equation is then normalised by the total enzyme concentration [ET]:

v

[ET]=

kcat[ES][ET]

(6.4)

With the maximum reaction velocity being defined as Vmax = kcat[ET],

equation 6.4 is written as:

v =Vmax[ES]

[ET](6.5)

which gives equation 6.6 when replacing [ES] and [ET] using equations 6.2

and 6.3:

v =Vmax[E][S]

Km[E](1 + [S]

Km+ [I]

Ki+ [S][I]

KmδKi

) (6.6)

Rewriting this equation yields equation 6.7 or 6.8:

v =Vmax[S]

Kmα + [S]β(6.7)

or

v =(Vmax/β)[S]

Km(α/β) + [S](6.8)

with

α = 1 +[I]Ki

(6.9)

and

β = 1 +[I]

δKi(6.10)

As can be seen from equation 6.8, linear mixed inhibition results in a decrease

of V ∗max (apparent Vmax) since V ∗

max = Vmax/β. The apparent Km (K∗m =

Km(α/β)) will increase according to Marangoni (2003) suggesting (α/β) and

consequently δ being greater than 1.

The reaction scheme for our proposed combined inhibition model can be

written similarly as for linear mixed inhibition:

Appendix 1 161

E + S Km←→ ES kcat−→ E + P

+ +

Ic Iul Kmc l Kmu

EIc ESIu

Unlike for linear mixed inhibition where the competitive and uncompeti-

tive inhibitor is considered to be one compound, the compound that interacts

with the enzyme (Ic) is different from the one that interacts with the enzyme-

substrate complex (Iu). When the reaction product [P] and [O2] are considered

as Ic and Iu, respectively, and the substrate S being O2 the dissociation constants

may be written as:

Km =[E][O2][EO2]

Kmc =[E][P][EP]

Kmu =[EO2][O2][EO2O2]

(6.11)

The rate equation is given by equation 6.1 and the total enzyme concentra-

tion of this reaction is as follows:

[ET] = [E] + [EO2] + [EP] + [EO2O2]

= [E] +[E][O2]

Km+

[E][P]Kmc

+[E][O2][O2]KmKmu

(6.12)

Normalisation of v by the total enzyme concentration and rearrangement

results in a similar model structure as for linear mixed inhibition:

v =Vmax[O2]

Kmα′ + [O2]β′(6.13)

or

v =(Vmax/β′)[O2]

Km(α′/β′) + [O2](6.14)

with α′ and β′ defined as follows:

α′ = 1 +[P]

Kmc(6.15)

and

β′ = 1 +[O2]Kmu

(6.16)

This results in a decrease of V ∗max (apparent Vmax) since V ∗

max = Vmax/β′.

The apparent Km (K∗m = Km(α′/β′)) will increase if (α′/β′) is greater than

162

one. This will be dependent on the values of Kmu, Kmc, [P] and [O2]. When

assuming [P] equal to [O2], (α′/β′) will be greater than one when Kmc <Kmu.

Appendix 2

In this appendix, the derivation of equation (4.20) (see Chapter 4) from the

set of ordinary differential equations as given in equation (4.14) (Chapter 4) is

explained.

Equation (4.14) was defined as a set of the following differential equations:

d[PPO]/dt = kPPO[P ][FA] (6.17)

d[P ]/dt = −kPPO[P ][FA] (6.18)

d[Q]/dt = kbrown[PPO][DP ] (6.19)

d[DP ]/dt = −kbrown[PPO][DP ] (6.20)

d[FA]/dt = ksen[FR] (6.21)

d[FR]/dt = ksen[FR] (6.22)

with the following initial conditions (at t=0):

[PPO] = PPO0 = 0

[P ] = P0

[Q] = Q0 = 0 (6.23)

[DP ] = DP0

[FA] = FA0 = 0

[FR] = FR0

The following mass balances can be derived from the coupled differential

equations ((6.17)-(6.18)), ((6.19)-(6.20)) and ((6.21)-(6.22)), respectively:

d[PPO]/dt = −d[P ]/dt (6.24)

d[Q]/dt = −d[DP ]/dt (6.25)

d[FA]/dt = d[FR]/dt (6.26)

163

164

Integration of these equations yields relations between [PPO] and [P], [Q]

and [DP], [FA] and [FR] respectively:

[PPO] = P0 − [P ] (6.27)

[DP ] = DP0 − [Q] (6.28)

[FR] = [FA] + FR0 (6.29)

Substitution of equation (6.27) and (6.28) in equation (6.19) yields:

d[Q]/dt = kbrown(P0 − [P ])(DP0 − [Q]) (6.30)

Substitution of equation (6.29) in equation (6.21) and solving the differential

equation, yields:

[FA] = FR0

(eksent − 1

)(6.31)

Note that equation (6.31) and all further derivations only apply for constant

environmental conditions.

This expression of [FA] is subsequently substituted in equation (6.18) which

yields:

d[P ]/dt = −kPPO[P ]FR0

(eksent − 1

)(6.32)

By solving this differential equation, the following expression for [P] can be

derived:

[P ] = P0 exp(

kPPOFR0

ksen

(1− eksent

)+ kPPOFR0t

)(6.33)

which was used in equation (6.30) to result in:

d[Q]/dt = kbrownP0

(1− exp

(kPPOFR0

ksen

(1− eksent

)+ kPPOFR0t

))· · ·

× (DP0 − [Q]) (6.34)

From equation (6.34) an expression can be found to describe the change of

L∗ as a function of time. In chapter 4, equation 4.18 it was stated that

L∗ = L∗0 −(

L∗0 − L∗

DP0

)[Q] (6.35)

Subsequently, dL∗/dt can be written as:

dL∗/dt = −L∗0 − L∗

DP0d[Q]/dt (6.36)

Incorporating the expression for d[Q]/dt (from equation (6.34)) in equation

(6.36) yields:

dL∗/dt = kbrownP0

(1− exp

(kPPOFR0

ksen

(1− eksent

)+ kPPOFR0t

))· · ·

×(L∗+∞ − L∗

)(6.37)

So finally, one differential equation is kept to describe the change in L∗-value

over time at constant environmental conditions.

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List of publications

International journal publications

Conesa, A., Artes-Hernandez, F., Geysen, S., Nicolaı, B. M. and Artes, F.

(2006). High oxygen combined with high carbon dioxide improves micro-

bial and sensory quality of fresh-cut peppers. Postharvest Biology and

Technology, Accepted.

Escalona, V. H., Geysen, S., Verlinden, B. E. and Nicolaı, B. M. (2006).

Microbial quality and browning of fresh-cut butter lettuce under superat-

mospheric oxygen conditions. European Journal of Horticultural Science,

Accepted.

Escalona, V. H., Verlinden, B. E., Geysen, S. and Nicolaı, B. M. (2006).

Changes in respiration of fresh-cut butter lettuce harvested in different

moments using high oxygen and moderate carbon dioxide controlled at-

mospheres. Postharvest Biology and Technology, 39, 48−55.

Geysen, S., Escalona, V. H., Verlinden, B. E., Aertsen, A., Geeraerd, A. H.,

Michiels, C. W., Van Impe, J. F. and Nicolaı, B. M. (2006). Validation

of predictive growth models describing superatmospheric oxygen effects

on Pseudomonas fluorescens and Listeria innocua on fresh-cut lettuce.

International Journal of Food Microbiology, 111, 48−58.

Geysen, S., Escalona, V. H., Verlinden, B. E. and Nicolaı, B. M. (2006).

Modelling the effect of superatmospheric oxygen and carbon dioxide con-

centrations on the respiration of fresh-cut butterhead lettuce. Journal of

the Science of Food and Agriculture, In press.

Geysen, S., Geeraerd, A. H., Verlinden, B. E., Michiels, C. W., Van Impe,

J. F. and Nicolaı, B. M. (2005). Predictive modelling and validation of

Pseudomonas fluorescens growth at superatmospheric oxygen and carbon

dioxide concentrations. Food Microbiology, 22, 149−158.

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182

Geysen, S., Verlinden, B. E., Geeraerd, A. H., Van Impe, J. F., Michiels

and C. W., Nicolaı, B. M. (2005). Predictive modelling and validation of

Listeria innocua growth at superatmospheric oxygen and carbon dioxide

concentrations. International Journal of Food Microbiology, 105, 333−345.

Gomez, P. A., Geysen, S., Verlinden, B. E. and Nicolaı, B. M. (2006). Mod-

elling the effects of superatmospheric oxygen concentrations on in vitro

mushroom PPO activity. Journal of the Science of Food and Agriculture,

In press.

Book chapters

Geysen, S., Verlinden, B. E. and Nicolaı, B. M. (2005). Thermal treatments

of fresh fruit and vegetables In: Jongen, W. (Ed.), Improving the safety of

fresh fruit and vegetables. Woodhead Publishing Unlimited, Cambridge,

UK, pp. 429−453.

Conference proceedings

Escalona, V. H., Geysen, S., Verlinden, B. E. and Nicolaı, B. M. (2005). Ef-

fect of high oxygen and carbon dioxide on the microbial quality of fresh-cut

butter lettuce. In: FRUTIC05: Information and technology for sustain-

able fruit and vegetable production, Montpellier, France. 111−118.

Escalona, V. H., Verlinden, B. E., Geysen, S and Nicolaı, B. M. (2005).

Effect of high oxygen and carbon dioxide controlled atmosphere conditions

on respiration and sensory quality of fresh-cut butter lettuce. In: Tanner,

D. J. and Day, B. P. F. (Eds.), Acta Horticulturae: Proceedings of the

international conference postharvest unlimited downunder 2004, Sydney,

Australia. 383−385.

Escalona, V. H., Verlinden, B. E., Geysen, S. and Nicolaı, B. M. (2005).

Quality changes of fresh-cut butter lettuce under superatmospheric oxy-

gen conditions. In: Acta Horticulturae: 9th International controlled at-

mosphere research conference, Michigan State University, East Lansing,

Michigan. In press.

Escalona, V. H., Verlinden, B. E., Geysen, S. and Nicolaı, B. M. (2005).

Superatmospheric oxygen condition combined with carbon dioxide lev-

els affect the respiration rate of fresh-cut butter lettuce. In: Acta Hor-

List of publications 183

ticulturae: 9th International controlled atmosphere research conference,

Michigan State University, East Lansing, Michigan. In press.

Geysen, S., Escalona, V. H., Verlinden, B. E. and Nicolaı, B. M. (2005).

Modelling respiration in fresh-cut butter lettuce as a function of carbon

dioxide, low and superatmospheric oxygen concentrations and tempera-

ture. In: Hertog, M. L. A. T. M. and Nicolaı, B. M. (Eds.), Acta Horticul-

turae: Proceedings of the Third international symposium on applications

of modelling as an innovative technology in the agri-food chain, Leuven,

Belgium. 545−551.

Geysen, S., Verlinden, B. E., Conesa, M. A. and Nicolaı, B. M. (2005). Mod-

elling respiration of strawberry (cv. ‘Elsanta’) as a function of temper-

ature, carbon dioxide, low and superatmospheric oxgyen concentrations.

In: 7th Fruit, Nut and Vegetable Production Engineering Symposium: In-

formation and technology for sustainable fruit and vegetable production,

Montpellier, France. 119−126.

Geysen, S., Verlinden, B. E., Escalona, V. H., Conesa, M. A. and Nicolaı,

B. M. (2005). Modelling respiration in fresh produce at superatmospheric

oxygen and carbon dioxide concentrations: general approach and case

study for strawberry and fresh-cut butterhead lettuce. In: Acta Hor-

ticulturae: 9th International controlled atmosphere research conference,

Michigan State University, East Lansing, Michigan. In press.

Geysen, S., Verlinden, B. E., Geeraerd, A. H., Michiels, C. W., Van Impe,

J. F. and Nicolaı, B. M. (2005). A validated predictive model for Listeria

innocua growth at superatmospheric oxygen and carbon dioxide concen-

trations. In: Hertog, M. L. A. T. M. and Nicolaı, B. M. (Eds.), Acta

Horticulturae: Proceedings of the Third international symposium on ap-

plications of modelling as an innovative technology in the agri-food chain,

Leuven, Belgium. 223−230.

Geysen, S., Verlinden, B. E., Geeraerd, A. H., Van Impe, J. F., Michiels,

C. W. and Nicolaı, B. M. (2003). A growth model for Pseudomonas fluo-

rescens at superatmospheric oxygen and elevated carbon dioxide concen-

trations. In: Martens, L., Steurbaut, W., Stevens, M., Van Cleemput, O.,

and Vandamme, E. (Eds.), Communications in agricultural and applied

biological sciences, 9th PhD Symposium on applied biological sciences,

Leuven, Belgium. 147−150.

184

Geysen, S., Verlinden, B. E., Geeraerd, A. H., Van Impe, J. F., Michiels,

C. W. and Nicolaı, B. M. (2003). Modelling the surface growth of Pseu-

domonas fluorescens at elevated oxygen and carbon dioxide concentra-

tions. In: Van Impe, J. F. M., Geeraerd, A. H., Legurinel, I., and Mafart,

P. (Eds.), Predictive modelling in foods, 4th International Conference on

Predictive modelling in foods, Quimper, France. 274−276.

Geysen, S., Verlinden, B.E., Michiels, C.W. and Nicolaı, B.M. (2004). Mush-

room quality under high oxygen modified atmospheres. Ageng 2004, En-

gineering the future, Leuven, Belgium., CD-Rom, Abstract nr. 418.

Gomez, P. A., Geysen, S., Verlinden, B. E. and Nicolaı, B. M. (2005). Mod-

elling in vitro mushroom PPO kinetics at superatmospheric oxygen con-

centrations. In: Nicolaı, B. M., Hertog, M. L. A. T. M., and Tijskens, L.

M. M. (Eds.), Acta Horticulturae: Proceedings of the third international

symposium on applications of modelling as an innovative technology in

the agri-food chain, Leuven, Belgium. 553−559.