Implementation of Quantitative Microbial Risk Assessment and Predictive Microbiology Methods for Food Safety Assurance Applications

Microbiological foodborne diseases cause significant burden of public health and jeopardize global and local economies. To tackle the overall disease burden, regulatory agencies set limits on microbial contamination in foods so that consumers will have access to safer food products. Historically, food safety regulations have been focused on monitoring the hazards within the production chains. However, with the shift from hazard based to risk based food safety approaches, public health risks from consuming contaminated products are focused to better identify the food safety measures and determine performance objectives. Quantitative microbial risk assessment and predictive microbiology methods serve as useful tools for estimating the changes in microbial contamination from farm-to-fork and the associated public health impact to aid risk based management of food safety. Therefore, this study aims to provide frameworks for applying quantitative microbial risk assessment and predictive microbiology to addressing food safety risks.In this dissertation, two projects are presented. The first project, covering three studies, focuses on risk assessment of Campylobacter contamination in broiler chicken supply chains. Systematic review and meta analysis were conducted to identify and quantify different risk mitigation options for the broiler supply chains. A quantitative microbial risk assessment model was developed to evaluate different mitigation scenarios. Then, cost effectiveness was investigated to balance between the costs of implementing interventions against the effectiveness in terms of reduction in contamination and public health impact. The second project, covering two studies, exemplifies the application of predictive microbiology for food industrial use. In this project, the survival of pathogenic bacteria of public health importance (Listeria monocytogenes, Escherichia coli O157:H7 and Salmonella spp.) in soy sauce based products were modeled based on existing and new challenge test data. The existing data provided information about the importance of various product properties on the survival of target pathogens. Then, a predictive model was developed for the survival of L. monocytogenes with the extension of a web based tool to aid risk management and research and development activities for the industry