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<UL> <LI>Evaluate, validate, and where necessary, develop new innovative, robust and valid predictive models for estimating the responses of microbial pathogens in select food matrices, as a function of: food formulation; competitive microbial flora; thermal inactivation; and process unit operations. <LI>Determine and elucidate objective measures to assess performance versus observation, and develop alternate responses or options. <LI> Determine what strategies should be used to predict the distribution of lag times, and the worst-case scenario. <LI>Expand Combase within CEMMI so that they can be exploited for model development and validation.
Approach: Quantitative data will be collected for the effects of selected environmental parameters on foodborne pathogen growth, survival and inactivation. Relevant environmental conditions will include food formulation, native microbial flora, inoculum level, bacterial history, and the effects of food process operations. Priority pathogen-food combinations will be identified through stakeholder interactions and by identifying sensitive data gaps in microbial risk assessment. Experimental data will be used to confirm and where necessary produce primary growth and inactivation models, as well as probabilistic models for growth/no growth interfaces and microbial transfer among food processing surfaces. Model performance will be described using independent validation data from ongoing experiments with food matrices, and microbiology databases, such as ComBase. The resulting technologies will be transferred to stakeholders via the ARS Pathogen Modeling Program and process risk model software.