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Predictive microbiology has become an important field in modern food safety research and an indispensable tool for assessing and managing the nation’s food safety risks. The data and modeling tools resulting from predictive microbiology research are used by the food industry for designing safe processing and handling practices and developing Hazard Analysis and Critical Control Points (HACCP) programs to ensure the microbiological safety of products, and by regulatory agencies for risk assessment and decision making to support food safety regulations. The food industry and regulatory agencies constantly need methodologies to validate existing and new models that can be confidently applied to various food systems. <P> Therefore, the objective of this project is to develop and validate robust predictive models that accurately describe the growth, survival, inhibition, and inactivation of high priority pathogens in meat, poultry, produce, and seafood products under complex intrinsic and extrinsic environments and particularly, under dynamic temperature conditions. This can be achieved by developing mechanistic models in combination with advanced numerical analysis techniques and neural network analysis to accurately predict microbial growth, survival, and inactivation in real food systems. Models developed from this research will be incorporated into the exiting USDA Pathogen Modeling Program and expand the ComBase microbiological database. <P> The results from these studies can be used by the food industry to evaluate the microbial safety of products and to enhance preventative measures (such as HACCP), and by regulatory agencies to conduct quantitative risk analysis and develop mitigation strategies and policies to improve the safety of U.S. food supply.
A large volume of microbiological data will be collected, analyzed, and used to build accurate, robust predictive models relevant to the real-world food products and conditions for use by risk assessors and managers in the food industry and regulatory agencies to effectively manage and mitigate microbial food safety risks. <P> Our research will improve the accuracy and reliability of predictive modeling and its application to solve real world problems, allowing the food industry to meet the increasing stringent regulatory performance standards, and to prevent and reduce the incidences and outbreaks of foodborne poisonings. <P> The models and data will assist FSIS and FDA in conducting risk assessment of food products and process deviations, and developing science-based food safety regulations and public policies. <P> This project will benefit food processors, especially those small and very small operations, to develop processes and product formulations that minimize the risks of foodborne outbreaks and extend product shelf-life, as well as will assist processors in complying with regulatory performance standards. As a result, safer foods will be produced and consumed, thus improving the safety and confidence of U.S. food supply.