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ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Residue Chemistry and Predictive Microbiology Research » Research » Research Project #430152

Research Project: Data Acquisition, Development of Predictive Models for Food Safety and their Associated Use in International Pathogen Modeling and Microbial Databases

Location: Residue Chemistry and Predictive Microbiology Research

Project Number: 8072-42000-079-00-D
Project Type: In-House Appropriated

Start Date: Jan 11, 2016
End Date: Jan 10, 2021

Objective:
Mathematical models that predict behavior of pathogens in food can be used to verify critical control points in Hazard Analysis and Critical Control Point (HACCP) programs. For example, they can be used to assess whether or not a process deviation results in a one log cycle increase of Clostridium perfringens during cooling of a cooked meat product during commercial processing. Models that predict behavior of pathogens can be integrated with data for pathogen contamination to predict dynamic changes in pathogen prevalence and number in food across unit operations of a production chain. Predictions of consumer exposure can then be used in a dose-response model to form a process risk model that predicts consumer exposure and response to pathogens in food produced by specific scenarios. Process risk models have great potential to improve food safety and public health by providing a better assessment of food safety and identification of risk factors. In the past, we have developed predictive models and process risk models that have proven highly useful in providing regulatory agencies and the food industry with an objective means of assessing food safety and identifying risk factors. The goal of the proposed research is to elevate that successful effort to the next level of sophistication by considering additional variables and developing new and improved models and more effectively transferring this new research to the food industry by providing updated and improved versions of our software products: the Predictive Microbiology Information Portal, ComBase, and the Pathogen Modeling Program. 1: Develop and validate predictive models for behavior of stressed and unstressed pathogens in food with added antimicrobials. This includes development of validated dynamic models for spores and vegetative foodborne pathogens for evaluating heating and cooling process deviations. 2: Develop and validate process risk models for higher risk pathogen and food combinations. 3: Expand and maintain the ARS-Pathogen Modeling Program and Predictive Microbiology Information Portal. Continue to support the development and utilization of ComBase with our associated partners the Institute of Food Research (IFR) and the University of Tasmania (UTas) as an international data resource.

Approach:
Effects and interactions of time, temperature, pH, acidulant, water activity, humectant, or preservatives (phosphates, organic acid salts, and nitrite) in meat and poultry products, as well as in rice, beans, and pasta will be assessed to collect kinetic data for pathogens (Listeria monocytogenes, Escherichia coli O157:H7, Staphylococcus aureus, Salmonella, Clostridium perfringens and Bacillus cereus). Kinetic data will be modeled using primary and secondary models. Predictive models performance will be evaluated using the acceptable prediction zone method. Once validated and published, predictive models will be incorporated into the Pathogen Modeling Program and data will be archived in ComBase. Kinetic data for development of predictive microbiology models for survival and growth of pathogens (Salmonella, E. coli O157:H7, Campylobacter jejuni, and Listeria monocytogenes) on higher risk food (tomatoes, lettuce, raw milk, and crab meat) will be obtained in inoculated pack studies. Pathogens will be enumerated on higher risk food during storage trials using an automated miniature most probable number method. Kinetic data will be modeled using neural network modeling methods and models will be validated against independent data using the acceptable prediction zone method. Whole sample enrichment real time polymerase chain reaction (WSE-qPCR) will be used to obtain data for prevalence, number, and types of pathogens on higher risk food. Predictive microbiology models and contamination data obtained by WSE-qPCR will be integrated to form process risk models that predict consumer exposure and response to pathogens on higher risk food produced by different scenarios. 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.