DE ZIN EN ONZIN VAN PREDICTIEVE MICROBIOLOGIE · 2017. 5. 15. · MODELS TO PREDICT SHELF LIFE...

DE ZIN EN ONZIN VAN PREDICTIEVEMICROBIOLOGIE

1

WHAT IS PREDICTIVE MICROBIOLOGY?

Use of mathematical equations to describe microbial

behaviour

2© UGent 2017

Growth

Inactivation

Survival

Spore germination

Toxin production

WHAT IS PREDICTIVE MICROBIOLOGY?

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MtBe

CAtx

1log

MtBCAtx expexplog

xxutdt

dxmax

RT

Ea

eAk

TTR

H

TTR

H

TR

HTC

Tk

HL

HL

A

11exp

11exp1

1

289

1exp

298)25(

)(

1

2/12/1

maxmin exp1 TTcTTbk

pHTpHT opt ,max

MODEL DEVELOPMENT

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1. Plan

Aim A priori knowledge

2. Data collection

3. Mathematical equation

A priori knowledge

4. Model validation

Model can be

accepted

Applic

atio

ns

MODEL DEVELOPMENT

Models for specific micro-organisms

very general models

includes mostly only pH, aw, temperature as variables

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MODEL DEVELOPMENT

Models for specific products

developed for specific spoilage organisms or

pathogens

includes more environmental variables, only in ranges

relevant for that food product

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MODEL DEVELOPMENT

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1. Plan


2. Data collection


A priori knowledge

4. Model validation

Model can be

accepted

Applic

atio

ns

DATA COLLECTION

Start from literature data

+ easy

+ fast

+ cheap

Laboratorium experiments

+ controlled

+ link between data

and model developers

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- black box

- quality check difficult

- models should be discussed

completely in the paper

- labour intensive

- expensive

MODEL DEVELOPMENT

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1. Plan


2. Data collection


A priori knowledge

4. Model validation

Model can be

accepted

Applic

atio

ns

KINETIC VERSUS PROBABILISTIC MODELS

Kinetic model: describes the evolution in cell density as

a function of time and as a function of the environmental

conditions

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INPUT OUTPUT

Temperature

pH

aw

Time

…

Lag time

Growth rate

KINETIC VERSUS PROBABILISTIC MODELS

Probabilistic model: describes the chance that a specific

phenomenon (e.g. toxin production, growth,…) occurs as

a function of the environmental conditions

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INPUT OUTPUT

Temperature

pH

aw

…

% chance to grow

90%

50%

10%

MODEL DEVELOPMENT

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1. Plan


2. Data collection


A priori knowledge

4. Model validation

Model can be

accepted

Applic

atio

ns

MODEL VALIDATION

Is the most appropriate model equation used to model

the obtained data?

Results mentioned in research study

Is the model validated in a (target) food product?

Is the model also valid for other food products?

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MODEL DEVELOPMENT

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1. Plan


2. Data collection


A priori knowledge

4. Model validation

Model can be

accepted

Applic

atio

ns

MOST USED AVAILABLE FREEWARE MODELS

FSSP from DTU

- originally developed for the seafood industry

- extended with data on meat products and cheese

- models for growth of L. monocytogenes and specific spoilage organisms,

taking into account their interaction

- model for histamine formation

- growth/no growth model for L. monocytogenes

Meat model from DMRI

- growth model for L. monocytogenes

- growth/no growth model for C. botulinum

- inactivation model for E. coli and Salmonella in fermented meat

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Advantages Disadvantages

- Easy to use - Limited applications

- Direct link with the peer

reviewed study

- To model the interactions in

FSSP background knowledge

is needed

- Many variables can be

combined

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ComBase:

- database with more than 50 000 records

- based on the database growth and inactivation models are developed to

predict microbial behaviour

Microbial Responses Viewer:

- based on ComBase database

- data are represented as iso-growth rate contour plots

- clear distinction between growth and no growth

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Advantages Disadvantages

- Many studies taken into

account

- No direct link between

predictions and study

- Easy to use - Lag phase can be chosen by

the user

- Limited amount of variables

that can be combined

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PREDICTION OF SALMONELLA AT 7°C

Combination of environmental conditions:

7°C

aw: 0.990

pH: 7.0

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± 6.0 log CFU/g growth within 20 days


This growth is unexpected as it is close to the minimal

growth temperature of Salmonella

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DATABASE


Input variables:

Temperature: 6 – 8°C

aw: 0.98 – 1.00

pH: 6.5 – 7.5

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56 results

39 studies (70%)

growth

17 studies (30%)

no growth


Results of MRViewer at pH 6.5 ± 0.5

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No growth Growth


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Experiments performed at LFMFP according to AEM

article

S. enterica in 5, 10 and 20% spinach or mix salad extract

NO GROWTH observed at 4°C (5 days)

S. Typhimurium and S. Thompson in 20% spinach or mix

salad extract and in nutrient broth

4°C: no growth after 15 days

7°C: no growth after 15 days

12°C: growth (> 5 log CFU/g) after 5 days


Combination of environmental conditions:

7°C

aw: 0.990

pH: 7.0

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± 6.0 log CFU/g growth within 20 days

Important to be critical on the

outcome of a predictive model

MODELS TO SUPPORT CHALLENGE TESTS

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L. monocytogenes in RTE-foods (EU-legislation N°2073/2005)

(i) RTE foods for infants and for special medical purposes

absence in 25 g

(iii) RTE foods unable to support growth, other than those intended for infants and for

special medical purposes

100 CFU/g (products placed on the market during their shelf-life)

(ii) RTE foods able to support growth, other than those intended for infants and for

special medical purposes

absence in 25 g (before the food has left the immediate control of the food producer)

100 CFU/g (products placed on the market during their shelf-life)


Food producers shall conduct studies in order to

investigate compliance with the criteria throughout the

shelf-life.

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Specifications for physico-chemical characteristics

Available literature

Challenge tests

Predictive microbiology


Interpretation by FAVV / AFSCA based on EU Technical

guidance

Challenge tests to assess growth potential:

1 batch in threefold or 3 batches in threefold

- Before 2017: decision based on interbatch calculator

(based on pH and aw measurements of 3 batches)

- From 2017: decision based on predictive models

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decision based on predictive models

Possible?

Standard physico- chemical measurements in

challenge tests : pH and aw

Is this enough?

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Case study: RTE product (meat) stored in MAP pH: 6.44

aw: 0.97

MAP: 60% CO2

T-profile: 20 days at 4°C – 8 days at 7°C

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COMBASE FSSP


Case study: RTE product (meat) stored in MAP pH: 6.44

aw: 0.97

MAP: 60% CO2

T-profile: 20 days at 4°C – 8 days at 7°C

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COMBASE FSSP

Results of the challengetest:

Growth potential: < 0.0 log CFU/g no growth


Case study: RTE product (meat) stored in MAP

Other preserving factors might be present

Residual nitrite?

Lactic acid?

Acetic acid?

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Complete study on the food product should be

performed by the company (product and

processing characteristics, shelf-life,…)

MODELS TO PREDICT SHELF LIFE

Conclusions of IWT project on cooked meat products

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IWT 130197

Variability on the initial contamination

is high between companies and within

a company

Often very low numbers



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Some products no growth of lactic

acid bacteria occured while the FSSP

model clearly showed growth

Shelf-life is underestimated

IWT 130197

Data point

Model fitting on the data

Model predictions



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IWT 130197

Some products showed lag phases

while the existing models do not

include predictions on lag phase

Growth rate was well predicted

Shelf-life is underestimated

IWT 130197

Data point


Model predictions



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IWT 130197

Existing models predicted too low

growth rate

Shelf-life is overestimated

IWT 130197

Data point


Model predictions



Existing models for lactic acid bacteria as specific

spoilage organism are not always applicable because:

Diversity of spoilers

Sometimes very low contamination of sliced meat

products individual lag time becomes important

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IWT 130197



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IWT 130197

LAB species N isolates (n=91) N products (n=41)

Lactobacillus sakei 38 19

Leuconostoc carnosum 19 9

Leuconostoc mesenteroides/sp. 9 5

Carnobacterium maltaromaticum/sp. 8 6

Lactobacillus fuchuensis 6 2

Weissella viridescens 5 3

Lactobacillus curvatus 3 1

Enterococcus devriesei 2 1

Lactococcus sp. 1 1

Results in collaboration with LM – UGent

and BCCM/LMG bacterial collection



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IWT 130197



Non-LAB species N isolates (n=50) N products (n=41)

Serratia sp. 24 10

Brochotrix thermosphacta 18 9

Bacillus cereus group 4 2

Bacillus subtilis group 1 1

Enterobacteriaceae 3 2



Almost half of the products contain Lactobacillus sakei

Particularly in cooked ham products (13/19)

In half of these products it is the only SSO (9/19)

Non-lactic acid bacteria are also important spoilers in sliced,

cooked meat products

Serratia sp. (24% of the products)

Brochotrix thermosphacta (22% of the products)

Co-isolation with lactic acid bacteria

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IWT 130197





Influence of the indivudual lag phase at 4°C

pH: 6.2, aw 0.975

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IWT 130197

18% showed no lag phase

Maximum lag: 12.7 days

Mean lag: 4.5 days



Influence of the indivudual lag phase at 7°C

pH: 6.2, aw 0.975

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IWT 130197

16% showed no lag phase

Maximum lag: 6.4 days

Mean lag: 2.3 days

MODELS TO PREDICT SHELF-LIFE

Growth/no growth model: SWEETSHELF

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aw

measured

pH

measured

Validation Model

8°C 15°C 22°C 8°C 15°C 22°C

G1A 0.869 5.70 G48 G38 G38 G G G

G2A 0.792 5.70 G90 G38 G38 NG G G

G2B 0.800 5.76 G90 G38 G38 NG G G

G2C 0.807 5.13 NG +/- G +/- G NG NG G/NG

G2D 0.810 5.11 NG NG +/- G NG NG G/NG

G2E 0.792 5.61 NG +/- G +/- G NG NG G/NG

G3A 0.844 4.72 G48 G38 G38 G G G

G3B 0.853 4.73 G48 G38 G38 G G G

G3C 0.861 4.83 NG NG NG NG NG NG

G3D 0.859 6.25 NG G38 G38 G G G

G4A 0.826 5.98 G90 G38 G38 G G G

IWT 110193

TAKE HOME MESSAGES

Use of predictive models can be very useful:

Fast

Give a good indication of the effect of environmental

conditions and particularly their combinations on the

growth of target micro-organisms

First screening of a product portfolio to make a risk

analysis

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“ A model is a usefull discussion partner giving you

good ideas, pointing you in the right direction, but like

other discussion partners, a model isn’t

always right” te Giffel, 1999

TAKE HOME MESSAGES

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TAKE HOME MESSAGES

Remain critical!

Models are not able to replace all microbial

analyses and food microbiologists

An VermeulenProject Manager

DEPARTMENT OF FOOD SAFETY AND FOOD QUALITY

E [email protected]

M +32 477 42 43 89

www.ugent.be

Ghent University

@ugent

Ghent University

DE ZIN EN ONZIN VAN PREDICTIEVE MICROBIOLOGIE · 2017. 5. 15. · MODELS TO PREDICT SHELF LIFE...

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Transcript of DE ZIN EN ONZIN VAN PREDICTIEVE MICROBIOLOGIE · 2017. 5. 15. · MODELS TO PREDICT SHELF LIFE...