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ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Microbial and Chemical Food Safety » Research » Research Project #439573

Research Project: Development and Validation of Predictive Models and Pathogen Modeling Programs; and Data Acquisition for International Microbial Databases

Location: Microbial and Chemical Food Safety

Project Number: 8072-42000-087-000-D
Project Type: In-House Appropriated

Start Date: Jan 11, 2021
End Date: Jan 10, 2026

Objective 1: Develop and validate predictive models for growth and/or lethality of high priority vegetative and spore-forming foodborne pathogens in foods with antimicrobial additives and variable pH. This includes development of models to evaluate potential food safety risks of cooking and cooling process deviations on meat product safety. The pathogens include and are not limited to E. coli O157:H7 and Shiga toxin-producing E. coli (STEC), L. monocytogenes, Salmonella spp., B. cereus, S. aureus, and C. perfringens. [C1, PS6] Objective 2: Acquire data, develop, and validate models for more accurate risk assessment of higher-risk pathogen and food combinations. [C1, PS6] Objective 3: Expand and maintain the ARS-Pathogen Modeling Program and Predictive Microbiology Information Portal. Continue to support the development and utilization of ComBase, an international data resource. [C1, PS6]

A Central Composite or a Full Factorial Design will be used to assess the effects and interactions of time, temperature, pH, acidulant, water activity, humectant, preservatives (phosphates, organic acid salts, and nitrite), strain, inoculum size, and previous history on pathogen behavior (growth, survival, death) in meat and poultry products, as well as in multicomponent products (i.e. rice, pasta, beans added to meat). The kinetic data collected for pathogens (Listeria monocytogenes, Escherichia coli O157:H7, STEC, Staphyloccus aureus, Salmonella, Clostridium perfringens, Bacillus cereus, Campylobacter jejuni) will be modeled using a series of primary and secondary models or artificial neural networks. Predictive models performance will be evaluated using the acceptable prediction zones method. Once validated and published, predictive models will be incorporated into the Pathogen Modeling Program and data will be archived in ComBase. All new models will be added to both versions of the Pathogen Modeling Program. A link to ARS, Poultry Food Assess Risk Models website will be provided in the portal. ComBase will be made compatible with the PMP.