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Microbial Modeling and Bioinformatics for Food Safety and Security


<OL> <LI> To evaluate, validate, and where necessary, develop new innovative, robust and valid
predictive models for the responses of microbial pathogens, including foodborne
threat agents, in select food matrices, as a function of: temperature, food
formulation, competitive microflora, physiological history, and surface transfer.
<LI>To develop novel approaches to assess model performance and robustness, leading to
more efficient strategies for producing and extrapolating models to different classes
of food.
<LI> To determine the probability distribution of lag phase duration (LPD) for foodborne
pathogens, as a function of the previous bacterial physiological history, to allow
risk managers to estimate worst-and best-case scenarios for pathogen behavior,
depending on likely sources of contamination; <LI>To identify molecular markers that
discriminate bacterial lag, growth and stationary phases, thus leading to more
mechanistic models and greater certainty for LPD prediction.

More information

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 vis the ARS Pathogen Modeling Program and process
risk model software.

Oscar, Thomas; Juneja, Vijay; Hwang, Cheng-An (Andy); Huang, Lihan; Bhaduri, Saumya; Sheen, Shiowshuh
USDA - Agricultural Research Service
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