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Digital farm-to-facility food safety testing optimization


Optimum food safety testing in the produce industry is limited by inconsistent requirements for product testing, legacy approaches focusing on single points in the supply chain, and inability of testing schemes to bound a contamination event. This project will create an integrated production, harvesting, processing, and packing model to define optimum food safety product testing schemes for produce. This project will address a research gap on how and when sampling and testing could, or could not, be used to detect and manage both point-source and systematic contamination as part of a Field-to-Facility food safety management system. First the project will build a Field-to-Facility computer model of leafy green produce safety testing, incorporating a range of alternative testing plans, potential processing impacts on pathogen risk, and contamination scenarios - defined in collaboration with academic and industry partners. Scenarios are based around a nominal 100,000 lb production lot of leafy greens, harvested in 10,000 lb sublots, subject to value-addition and packing, and tested by multiples of representative N60 composite samples taken at different supply chain points. Second, the model will represent a variety of higher-risk commodities with distinct risk profiles and risk-management options, specifically tomatoes, apples, jalapenos, and cilantro. This adaptation will involve site visits to major production regions in FL, WA, MI, SC, and CA to qualitatively assess the supply chain, literature review to estimate parameters, and discussions with industry partners to define additional product testing plans. Third, the project will optimize testing across the supply chain of each commodity. This is done by simulating 1,000s of variability and uncertainty iterations of field contamination, testing, and interventions, and calculating residual pathogens in the system. Then using these results for formal sensitivity analysis and optimization to define which sampling plans best reduce food safety risk across a range of contamination scenarios. The goal of this project is to affect real change in produce food safety testing though industry, academic, and regulatory use of both the model and published results to advocate for optimized testing in practice.

Matthew Stasiewicz
University of Illinois - Urbana-Champaign
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