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An experimental protocol to validate secondary-model application to foods was suggested. Escherichia coli, Listeria monocytogenes, Bacillus cereus, Clostridium perfringens, and Salmonella were observed in various food categories, such as meat, dairy, egg, or seafood products. The secondary model validated in this study was based on the gamma concept, in which the environmental factors temperature, pH, and water activity (a w ) were introduced as individual terms with microbe-dependent parameters, and the effect of foodstuffs on the growth rates of these species was described with a food-and microbe-dependent parameter. This food-oriented approach was carried out by challenge testing, generally at 15 and 10°C for L. monocytogenes, E. coli, B. cereus, and Salmonella and at 25 and 20°C for C. perfringens. About 222 kinetics in foods were generated. The results were compared to simulations generated by existing software, such as PMP. The bias factor was also calculated. The methodology to obtain a food-dependent parameter (fitting step) and therefore to compare results given by models with new independent data (validation step) is discussed in regard to its food safety application. The proposed methods were used within the French national program of predictive microbiology, SymPrevius, to include challenge test results in the database and to obtain predictive models designed for microbial growth in food products.Predictive microbiology has proven its value for a useful model-based description of microbial growth in foods ever since its development (18,19). Data used in building a model are usually acquired in laboratory media. However, the predictions agree more or less successfully with observations of food products (6, 36), and validation of the model proves to be necessary in such cases. Salter et al. (31) underlined the importance of good prediction for food safety, although their model was not validated in their paper. Indeed, models should be validated for prediction in the product in question, to allow for risk assessment (26). This is all the more important when creating a software application (9, 16), such as the French national program of predictive microbiology, SymЈPrevius (14). In this research program, industry, public, university, and technical center laboratories first constructed a parameter database covering 50 bacterial strains of five species grown in laboratory medium (20,21). The pathogenic bacteria selected were Escherichia coli, Listeria monocytogenes, Bacillus cereus, Clostridium perfringens, and Salmonella. This initial work led to a model describing growth rates versus temperature, pH, and water activity.The objective of this study was to develop a methodology to use these first results in foods. Thus, challenge tests were carried out, and then kinetics were analyzed to (i) obtain medium-dependent parameters and (ii) validate complete models. Since temperature is the major factor of interest in the food industry (18), the studies reported focused on that aspect. MATERIALS AND METHODSPrevio...

Describing the Sym'Previus project, the software and its deliverable facilities is the aim of this present paper. This software concerns all the partners of the food industry who are involved in the management of food safety and allows food-borne pathogen behaviour in food to be predicted, as function of the environment (nature of the food, manufacturing process, conditions of conservation). This analysis of microbial behaviour has been possible thanks to the progress made in predictive microbiology since the 1980s. Sym'Previus offers to food industry professionals and their partners the possibility of applying this progress, by giving access to a database, to simulation systems and expertise.

The management of microbial risk in food products requires the ability to predict growth kinetics of pathogenic microorganisms in the event of contamination and growth initiation. Useful data for assessing these issues may be found in the literature or from experimental results. However, the large number and variety of data make further development difficult. Statistical techniques, such as meta-analysis, are then useful to realize synthesis of a set of distinct but similar experiences. Moreover, predictive modeling tools can be employed to complete the analysis and help the food safety manager to interpret the data. In this article, a protocol to perform a meta-analysis of the outcome of a relational database, associated with quantitative microbiology models, is presented. The methodology is illustrated with the effect of temperature on pathogenic Escherichia coli and Listeria monocytogenes, growing in culture medium, beef meat, and milk products. Using a database and predictive models, simulations of growth in a given product subjected to various temperature scenarios can be produced. It is then possible to compare food products for a given microorganism, according to its growth ability in these products, and to compare the behavior of bacteria in a given foodstuff. These results can assist decisions for a variety of questions on food safety.

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