Location: Residue Chemistry and Predictive Microbiology Research
Project Number: 8072-42000-092-000-D
Project Type: In-House Appropriated
Start Date: Apr 6, 2021
End Date: Apr 5, 2026
Objective 1: Utilize one-step dynamic modeling and Bayesian analysis for prediction of growth and survival of foodborne pathogens throughout the supply chain. Objective 2: Utilize logistic modeling for determination of growth and no-growth boundary of high-risk pathogens in ready-to-eat foods. Objective 3: Utilize finite element analysis for prediction of bacterial growth and survival during food processing. Objective 4: User-friendly tools for predictive modeling.
A new dynamic approach will be developed and optimized to simulate and predict the growth and survival of major foodborne pathogens in meat and poultry products exposed to complex changes in the environmental conditions during heating, cooling, and storage. The research will utilize an advanced computational framework and probabilistic Monte Carlo simulation to analyze the dynamic changes in the population of foodborne pathogens, and will develop an expert decision support system to assist the food industry and regulatory agencies in making scientifically sound food safety decisions for products of concern. This project will continue to improve and upgrade the USDA Integrated Pathogen Modeling Program (IPMP) for data analysis, and develop a new data analysis tool, IPMP Global Fit, that minimizes the global residual errors for curve-fitting of growth and survival curves.