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ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Residue Chemistry and Predictive Microbiology Research » Research » Publications at this Location » Publication #325536

Research Project: DEVELOPMENT OF PREDICTIVE MICROBIAL MODELS FOR FOOD SAFETY AND THEIR ASSOCIATED USE IN INTERNATIONAL MICROBIAL DATABASES

Location: Residue Chemistry and Predictive Microbiology Research

Title: Dynamic kinetic analysis of growth of Listeria monocytogenes in a simulated comminuted, non-cured cooked pork product

Author
item Huang, Lihan

Submitted to: Food Control
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/27/2016
Publication Date: 6/29/2016
Citation: Huang, L. 2016. Dynamic kinetic analysis of growth of Listeria monocytogenes in a simulated comminuted, non-cured cooked pork product. Food Control. 71(2017):160-167. DOI:10.1016/j.foodcont.2016.06.043.

Interpretive Summary: Listeria monocytogenes is a major foodborne pathogen frequently associated with ready-to-eat (RTE) meat and poultry products regulated by the USDA Food Safety and Inspection Service. This study develops a new method to develop mathematical models to predict the growth of this pathogen in cooked pork. The models developed in this study are accurate and can be used in risk assessment and management of this pathogen in RTE meat and poultry products.

Technical Abstract: The objective of this study was to directly construct a tertiary growth model for Listeria monocytogenes in cooked pork and simultaneously determine the kinetic parameters using a combination of dynamic and isothermal growth curves. Growth studies were conducted using a cocktail of 5 strains of L. monocytogenes in cooked pork under both dynamic and isothermal temperature profiles designed to examine the effect of temperature of bacterial growth. A direct kinetic analysis method was used to construct the growth models and determine the kinetic parameters. The bacterial growth was simulated by a set of differential equations, and the temperature effect evaluated by the Huang square-root model. A numerical analysis and optimization method was used to simultaneously solve the different equations and search for the best fits of kinetic parameters for the growth models. The estimated minimum and maximum growth temperatures were 3.02 and 44.0 degree C, matching well with typical growth characteristics of this microorganism. The root-mean-square error (RMSE) of the optimization was 0.13 Log CFU/g. The growth models and kinetic parameters were validated using both dynamic and isothermal temperatures to check the accuracy of the models. The results showed that the RMSE of the predictions was 0.51 Log CFU/g. The residual errors of predictions follow a Laplace distribution, with 74.6% of the residual errors falling within plus or minus 0.5 Log CFU/g of the observations. This study proves that the one-step dynamic analysis with both dynamic and isothermal temperature profiles can be an effective approach for simultaneously constructing a tertiary model and determining the kinetic parameters.