Modeling in food microbiology : from predictive microbiology to exposure assessment

104 Pages·2016·6.721 MB·English

by  Membré, Jeanne-Marie, Valdramidis, Vasilis P

Modeling in Food Microbiology
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Modeling and Control of Food Processes Set
coordinated by
Jack Legrand, Gilles Trystram

Modeling in Food
Microbiology

From Predictive Microbiology
to Exposure Assessment

Edited by

Jeanne-Marie Membré
Vasilis Valdramidis
First published 2016 in Great Britain and the United States by ISTE Press Ltd and Elsevier Ltd
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as
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The rights of Jeanne-Marie Membré and Vasilis Valdramidis to be identified as the authors of this work
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British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
Library of Congress Cataloging in Publication Data
A catalog record for this book is available from the Library of Congress
ISBN 978-1-78548-155-0

Printed and bound in the UK and US
Contents
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
Jeanne-Marie MEMBRÉ and Vasilis VALDRAMIDIS
Chapter 1. Predictive Microbiology . . . . . . . . . . . . . . . . . . . . . 1
Vasilis VALDRAMIDIS
1.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3. Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4. The modeling cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4.1. Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4.2. Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4.3. Mathematical description . . . . . . . . . . . . . . . . . . . . . . . 6
1.4.4. Model validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5. Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.7. Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Chapter 2. Quantifying Microbial Propagation . . . . . . . . . . . . . . 17
Enda CUMMINS
2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2. Probability processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2.1. Binomial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2.2. Beta distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2.3. Negative binomial . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2.4. Poisson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
vi Modeling in Food Microbiology
2.3. Uncertainty (U) and variability (V) . . . . . . . . . . . . . . . . . . . 26
2.3.1. Definition and interest of incorporating them in the model . . . . . 26
2.4. Modeling propagation using a modular model . . . . . . . . . . . . . . 26
2.4.1. Incorporation of probability distributions
into predictive models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.4.2. Monte Carlo simulation . . . . . . . . . . . . . . . . . . . . . . . . 28
2.5. Separation of uncertainty and variability
when building a model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.7. Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Chapter 3. Modeling Microbial Responses:
Application to Food Spoilage . . . . . . . . . . . . . . . . . . . . . . . . 33
Jeanne-Marie MEMBRÉ and Stéphane DAGNAS
3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2. Modeling spoilage: application to
commercial sterility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2.1. Context: commercial sterility and G. stearothermophilus . . . . . . 35
3.2.2. Modeling framework: modular process risk model . . . . . . . . . 36
3.2.3. Model set up: choices of inputs and simulation procedure . . . . . 39
3.2.4. Probabilistic toolbox: second-order Monte
Carlo analysis, sensitivity and scenario analysis . . . . . . . . . . . . . . 40
3.3. Modeling spoilage: application to best-before-date
determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.3.1. Context: mould spoilage of food products . . . . . . . . . . . . . . 42
3.3.2. Factors affecting the mould growth on bakery-type
products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3.3. Primary and secondary models developed
(specifically) for mould spoilage . . . . . . . . . . . . . . . . . . . . . . . 44
3.3.4. Use of models to derive management options
which enable us to prevent/control food spoilage . . . . . . . . . . . . . . 47
3.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.5. Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Chapter 4. Modeling Microbial Responses:
Application to Food Safety . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Maria GOUGOULI and Konstantinos KOUTSOUMANIS
4.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.2. Risk-based food safety management . . . . . . . . . . . . . . . . . . . . 63
4.2.1. Predictive microbiology in a risk-based approach . . . . . . . . . . 65
4.2.2. Modeling microbial responses: application to food safety . . . . . 70
Contents vii
4.3. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.4. Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Jeanne-Marie MEMBRÉ and Vasilis VALDRAMIDIS
List of Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
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Introduction

The “traditional” food quality and safety management approach is based
on end-product testing. However, end-product testing gives only very limited
information on the safety status of a food. The food safety crisis of the 1990s
(e.g. Listeria, Salmonella, Escherichia coli, Campylobacter, dioxins,
antibiotics, acrylamide, BSE, etc.) revealed a failure in this traditional
approach. If a hazardous organism is found, it means something; but its
absence in a limited number of samples is no guarantee for the safety of a
whole production batch. End-product testing is often too little and takes place
too late [ZWI 16]. Nowadays, effective food quality and safety management
systems should rely on prerequisite programs (e.g. good manufacture
practices and good hygiene practices), the Hazard Analysis Critical Control
Points (HACCP) plan as well as on quantitative tools, namely predictive
microbiology and risk assessment approaches.
The development and application of models in food safety and food
spoilage falls within the discipline of predictive (food) microbiology, also
called predictive modeling in foods [VAN 00, VAN 03], or quantitative
microbial ecology [ROS 99]. Schaffner and Labuza [SCH 97] defined this
area as the gathering of the disciplines of food microbiology, engineering and
statistics to provide useful predictions about microbial behavior in food
systems. It, therefore, involves the accumulation of qualitative and
quantitative information on microbial behavior in foods and an increased
understanding of the microbial physiology [MCM 02a]. Predictive
microbiology has a proactive nature in contrast with the retrospective
surveillance resting on the prevention of food-borne disease [MCM 02b].

Introduction written by Jeanne-Marie MEMBRÉ and Vasilis VALDRAMIDIS.