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Meta‐Analysis of Food Safety Information Based on a Combination of a Relational Database and a Predictive Modeling Tool
Author(s) -
Vialette Michèle,
Pi Anthony,
Leporq Benoît,
Dervin Catherine,
Membré JeanneMarie
Publication year - 2005
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.0272-4332.2005.00568.x
Subject(s) - food safety , variety (cybernetics) , listeria monocytogenes , food safety management , computer science , database , biochemical engineering , data science , data mining , risk analysis (engineering) , food science , engineering , biology , bacteria , artificial intelligence , business , genetics
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.