New microbial modeling and bioinformatics techniques under development.

USDA-ARS scientists are in the process of validating and developing innovative, robust predictive models for analyzing the responses of microbial pathogens, including foodborne threat agents, in certain food matrices. The responses would be as a function of: temperature, food formulation, competitive microflora, physiological history and surface transfer.

The researchers also want to develop novel approaches that could be used to assess the performance and robustness of their microbial models, leading to more efficient strategies for producing and extrapolating models to different classes of food. They are determining the probability distribution of lag phase duration (LPD) for foodborne pathogens, as a function of previous bacterial physiological history. As you may know, the lag phase is the time required for the cell population to adjust to the food environment and begin to grow.

This determination would allow risk managers to estimate worstand best-case scenarios for pathogen behavior, depending on likely sources of contamination. Another objective of investigators is to identify molecular markers that discriminate bacterial lag, growth and stationary phases, thus leading to more mechanistic models and greater certainty for LPD prediction.

Quantitative data have been collected covering the effects of selected environmental parameters on the growth, survival and inactivation of foodborne pathogens. Relevant environmental conditions will include food formulation, native microbial flora, inoculum level, bacterial history and the effects of food process operations.

Scientists are identifying priority pathogen-food combinations through interactions with colleagues and by examining sensitive data gaps in microbial risk assessment programs. Experimental data will be used to confirm, and where necessary produce, primary growth and inactivation models, as well as probabilistic models covering growth-no growth interfaces and microbial transfer among food processing surfaces.

Researchers will analyze the performance of their models using independent validation data from ongoing experiments with food matrices and microbiology databases, such as ComBase. ComBase is a relational database of predictive microbiology information. ComBase contains thousands of data sets that describe the growth, survival and inactivation of bacteria under diverse environments relevant to food processing operations.

The resulting technologies will be transferred to industry using the ARS Pathogen Modeling Program and process risk model software.

Further information. Vijay K. Juneja, USDA-ARS Microbial Food Safety Research Unit, Eastern Regional Research Center, Room 2129.3, 600 E. Mermaid Lane, Wyndmoor, PA 19038; phone: [phone]; fax: [phone]; email: [e-mail].