application of alternative food-preservation - Bentham Science

Application of Alternative Food-Preservation Technologies to Enhance Food Safety & Stability, 2010, 161-187 161
Antonio Bevilacqua, Maria Rosaria Corbo and Milena Sinigaglia (Eds)
All rights reserved - © 2010 Bentham Science Publishers Ltd.
CHAPTER 10
Food Shelf Life and Safety: Challenge Tests, Prediction and Mathematical
Tools
Antonio Bevilacqua* and Milena Sinigaglia
Department of Food Science, Faculty of Agricultural Science, University of Foggia, Italy
Abstract: Predictive microbiology (PM) is an interesting tool to predict the survival/growth of pathogens and
spoiling microorganisms in foods, as well as a powerful mean for the evaluation of the shelf life and the
effects of some hurdles in food industry. This chapter offers an overview of the most important primary
models to fit growth (Baranyi, Gompertz and lag-exponential equations) or survival kinetics (the function of
Bigelow, along with the equations included in the Add-in-Excel component GInaFiT).
Then, the chapter describes some secondary models used in food microbiology (square root, cardinal,
Arrhenius and polynomial equations), as well as a brief synopsis of a new approach, the S/P model, based on
the simultaneous evaluation of the microflora growth and the production of an end-product or the
consumption of a substrate.
Another interesting tool proposed by the chapter is a summary of the approaches used for the evaluation of
the lag phase of a microbial population, along with an appendix reporting some key-concepts of the Design of
Experiments and a description of the indices for the evaluation of the goodness of fitting of a function.
Key-concepts: Challenge tests, Primary model (Gompertz, Baranyi and lag-exponential equations), Inactivation
curves, Secondary models, Growth/no growth model, Design of experiments.
PREDICTIVE MICROBIOLOGY: AN INTRODUCTION
Many authors cite the papers of Bigelow [1, 2] as the date of birth of predictive microbiology (PM) and the
beginning of use of this kind of approach to predict pathogen growth and/or survival in food industry, especially
in canning industry [3, 4]. For the next 30-40 years PM remained quiescent, due to the lack of tools to use
practically the concepts of microbial modeling in food industry; this impasse was overcome in 1970s-1980s and
a renaissance of PM started, due to the development and diffusion of electronic technologies, which enabled
continual monitoring of time and temperature throughout food chain and made easier to solve mathematical
equations quickly [4].
In 1983 Roberts and Jarvis published the first review in the field of PM and coined the term “predictive
microbiology”, as an interesting tool to predict pathogen survival and growth in food.
Since the 1980s PM has been dominated by the dichotomy between the use of kinetic equations and probability
models to predict the probability of growth of Clostridium botulinum and other toxinogenic microorganisms in
food; this dichotomy has resulted in the definition by Bridson and Gould [5] of two branches: the classical
microbiology, opposed to the quantal microbiology, where uncertainty dominates [3].
Devlieghere et al. [6] reported a brief synopsis of the basic concepts of PM and elucidated some parameters to
classify models for microbial growth:
1. the approach (empirical versus mechanistic).
2. the level (primary, secondary or tertiary models)
3. kind of inactivation (thermal and non-thermal inactivation).
Regarding the kind of approach, empirical models are not based on theoretical assumptions; they are built on
the basis of the data of a challenge test and cannot be extended to other situations. Examples of empirical models
are the Arrhenius and the polynomial equations.
*Address correspondence to this author Antonio Bevilacqua at: Department of Food Science, Faculty of Agricultural Science, University
of Foggia, Italy; E-mail: a.bevilacqua@unifg.it