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Robustness of Predictive Models for Listeria Growth and Inactivation in Ready-to-Eat Meats and Poultry

Objective

<OL> <LI> To conduct a comprehensive and critical review of published data and models related to growth and thermal inactivation of L. monocytogenes in ready-to-eat meat and poultry products, <LI> To conduct a rigorous cross-validation of published models (both broth-based and product-based) against independent data reported in a wide range of other studies with meat and poultry products, <LI> To conduct an evaluation of the relative impact of various intrinsic and extrinsic factors on the accuracy of predictive models for pathogen Listeria growth and inactivation, particularly in ranges requiring extrapolation.

More information

A comprehensive review of the literature will be conducted that reports predictive models and/or data for growth and/or thermal inactivation of L. monocytogenes, emphasizing RTE meat and poultry products. Information will be collated, reviewed, and summarized to describe L. monocytogenes model performance. Protocols will be developed protocols for evaluation of model performance, particularly for extrapolation. Preliminary comparison will be made of predictions from the USDA-ARS Pathogen Modeling Program and the U.K. Food MicroModel to literature data for growth and inactivation in the target food matrices. Available raw data will be extracted from previous literature, and from researchers with additional accessible data associated with but not included in previously published articles. A database will be developed for aggregating all of the collected data and modeling parameters into a uniform format that accounts for all of the relevant substrate, process, and experimental parameters for those data. These data will be merged into ComBase. Previously published models will be evaluated via a cross-validation that generates a standard error of prediction against all other relevant data sets in the database. A response surface analyses will be conducted to describe the standard error of prediction for each model in terms of the deviations/extrapolations from the original model domain. A journal article will be submitted summarizing the results of the above analysis. A yearly and final report will be submitted to ARS-NPS and NAFS.

Investigators
Tamplin, Mark
Institution
Michigan State University
Start date
2002
End date
2004
Project number
1395-42000-041-05S
Accession number
405937
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