Project Number: 1935-42000-057-00
Project Type: Appropriated

Start Date: Feb 08, 2006
End Date: Feb 06, 2011

Objective:
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. 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. 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; To identify molecular markers that discriminate bacterial lag, growth and stationary phases, thus leading to more mechanistic models and greater certainty for LPD prediction.

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.