Title: Plenary lecture: innovative modeling approaches applicable to risk assessments Author
Submitted to: Food Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 24, 2010
Publication Date: June 1, 2011
Citation: Oscar, T.P. 2011. Plenary lecture: innovative modeling approaches applicable to risk assessments. Food Microbiology. 28:777-781. Interpretive Summary: Currently food safety objectives and microbial performance standards applied at the processing plant are used to identify safe and unsafe food. This approach to food safety is supported by risk assessments that assume a single risk pathway after food leaves the processing plant and that do not employ the rare events’ modeling approach. However, this approach to food safety only on rare occasions is successful at identifying unsafe food because it fails to consider differences in pathogen virulence and post-process risk factors among processing plants. In contrast, by implementing the rare events’ modeling approach described in this paper, a risk assessment based approach that considers differences in pathogen virulence and post-process risk factors among processing plants in its evaluation of food safety can be implemented and will result in a better assessment and management of food safety risks.
Technical Abstract: Proper identification of safe and unsafe food at the processing plant is important for maximizing the public health benefit of food by ensuring both its consumption and safety. Risk assessment is a holistic approach to food safety that consists of four steps: 1) hazard identification; 2) exposure assessment; 3) hazard characterization; and 4) risk characterization. Risk assessments are modeled by mapping the risk pathway as a series of unit operations and associated pathogen events and then using probability distributions and a random sampling method to simulate the rare, random, variable and uncertain nature of pathogen events in the risk pathway. To properly model pathogen events, a rare events’ modeling approach is needed that links a discrete distribution for incidence of the pathogen event with a continuous distribution for extent of the pathogen event. When applied to risk assessment, rare events’ modeling leads to the conclusion that the most highly contaminated food at the processing plant does not necessarily pose the highest risk to public health because of differences in post-processing risk factors among distribution channels and consumer populations. Predictive microbiology models for individual pathogen events can be integrated with risk assessment models using the rare events’ modeling method.