A team of researchers from the University of Illinois Urbana-Champaign (UIUC) and Cornell University is developing a flexible computer model designed to evaluate contamination risks and control strategies throughout the produce supply chain. Supported by the Center for Produce Safety (CPS), the project aims to assist the food industry in comparing potential contamination scenarios and implementing effective control measures.
The research is led by Dr. Matthew Stasiewicz of UIUC, with co-principal investigator Dr. Martin Wiedmann of Cornell University and doctoral candidate Gabriella Pinto from UIUC’s Department of Food Science and Human Nutrition. The team initially focused on modeling supply chain risks for leafy greens contaminated by Shiga toxin-producing Escherichia coli (STEC), given the public health significance and existing data for this pathogen-commodity pair. They are now extending their work to include risk modeling for Salmonella in melons and aim to develop a model for Listeria monocytogenes in leafy greens.
The model framework encompasses five stages of the produce supply chain: primary production, harvesting, processing, retail, and consumer handling. Users can input various factors at each stage to estimate contamination probabilities, assess the impact of different interventions, and compare the effectiveness of control strategies. The output measures the risk of a product testing positive for microbial contamination upon reaching consumers.
In their analysis, the researchers compared the effectiveness of improved process controls, such as enhanced washing procedures, against additional product testing at the end of processing. Findings suggest that improved process controls provide a greater reduction in the overall risk of contamination at the retail level compared to increased end-product testing. While end-product testing can reduce public health and recall risks, it may lead to the rejection of many low-risk lots, as entire product lots are discarded when contamination is detected.
To facilitate industry application, the team has developed an interactive webpage, SCRM-Lite, allowing users to explore various contamination scenarios and intervention strategies based on the published test cases. This tool aims to support produce groups, associations, and policymakers in making informed decisions regarding food safety practices and resource allocation.
The project also involves collaboration with industry stakeholders to identify critical control points and assess the impact of small-scale deviations from best practices, such as irrigation water treatment failures, incomplete harvester sanitation, minor animal intrusions, and inadequate wash water control. Engagement with the CPS Industry Advisory Council and discussions at CPS Annual Symposiums have provided valuable insights to refine the model and ensure its practical relevance to industry needs.
As the project progresses, the researchers plan to continue refining the model to address various contamination scenarios and control strategies across different produce commodities. The ultimate goal is to equip the produce industry with a robust tool to estimate microbial risks, optimize food safety interventions, and enhance public health outcomes.
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