Meta-analysis of food safety information based on a combination of a relational database and a predictive modeling tool.
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.
Vialette
Michèle
M
Institut Pasteur de Lille, 1 rue du Professeur Calmette, BP 245, 59019 Lille cedex, France. michele.vialette@pasteur-lille.fr
Pinon
Anthony
A
Leporq
Benoît
B
Dervin
Catherine
C
Membré
Jeanne-Marie
JM
D016208:Databases, Factual; D004926:Escherichia coli / Q000378:metabolism; D004927:Escherichia coli Infections / Q000378:metabolism; D005506:Food Contamination; D005511:Food Handling; D005516:Food Microbiology; D018857:Food Packaging; D006863:Hydrogen-Ion Concentration; D007700:Kinetics; D008089:Listeria monocytogenes / Q000378:metabolism; D008088:Listeriosis / Q000378:metabolism; D008460:Meat; D008461:Meat Products / Q000382:microbiology; D008962:Models, Theoretical; D012306:Risk; D013696:Temperature; D013997:Time Factors