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ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Residue Chemistry and Predictive Microbiology Research » Research » Research Project #430595

Research Project: Development of Predictive Microbial Models for Food Safety using Alternate Approaches

Location: Residue Chemistry and Predictive Microbiology Research

2016 Annual Report


1a. Objectives (from AD-416):
The main goal of this project is to develop and validate more accurate and robust mathematical models and computational algorithms for predicting the growth of human pathogens in processed foods exposed to complex processing, distribution, and storage conditions. This project focuses on applying and improving a new one-step dynamic kinetic analysis methodology and optimization method to generate kinetic models. We will develop models for foods, pathogens, and environmental factors that are not in duplication of the existing models in the USDA PMP or products of other research institutions. We will continue to optimize a new dynamic approach aiming at more accurate and rapid estimation of kinetic parameters by direct construction of predictive models for foodborne pathogens. This direct approach, recently applied in experiments for determining the growth kinetics of Clostridium perfringens, was very accurate in predicting its growth in cooked beef during cooling in the validation studies. We also experimented with a probabilistic approach to predict the growth of C. perfringens. In the next five years, we will continue to optimize the methodology, experimental design, and computational algorithms for determining the growth kinetics of other high-priority pathogens, such as Listeria monocytogenes, Salmonella spp., pathogenic Escherichia coli, C. perfringens, and Bacillus cereus, in various types of food products. We will continue to examine and expand the application of probabilistic simulation methods for process risk assessment, real-time food safety decision-making, and quality control. Furthermore, we will continue to support the scientists in the predictive microbiology community by providing more user-friendly, comprehensive, and robust interactive tools for data analysis for application in research and education. This research will fill the gap between the immense need in the nation for predictive modeling and the availability of highly accurate dynamic and probabilistic modeling methods and tools. Therefore, the specific objectives of this project include: 1: Development and validation of predictive models for growth of high priority pathogens in processed foods. 2: Dynamic simulation and probabilistic modeling of growth of foodborne pathogens in foods. 3: Develop an advanced decision support system and software for predictive microbiology and food safety regulations. 4: Further, expand where necessary the ARS curve-fitting (modeling) program also known as the “Integrated Pathogen Modeling Program (IPMP)”.


1b. Approach (from AD-416):
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.


3. Progress Report:
This new Project Plan was recently certified through the ARS Office of Scientific Quality Review (OSQR). For further details on current work see the 2016 annual report for project 8072-42000-075-00D.


4. Accomplishments


5. Significant Activities that Support Special Target Populations:
None.