With support from the Center for Produce Safety (CPS), researchers are developing a flexible computer model that enables food industry users to compare potential contamination risks along the supply chain and control strategies, and also allows users to run “what-if” scenarios to weigh the impacts of current practices against newly identified risks or control strategies.

The ongoing project is led by University of Illinois Urbana-Champaign’s (UIUC’s) Matthew Stasiewicz, Ph.D., a member of the Food Safety Summit Educational Advisory Board. He was joined by co-principal investigator Martin Weidmann, Ph.D. of Cornell University, as well as Gabby Pinto, an M.P.H. and Ph.D. candidate at UIUC.

The team first modeled supply chain risks for leafy greens contaminated by Shiga toxin-producing Escherichia coli (STEC), due to the public health significance of and existing data for the pathogen-commodity pair. They are now working on comparisons for small-scale deviations from best practices, and have also begun risk modeling for Salmonella and melons. Another goal of the research project is to develop a risk model for Listeria monocytogenes in leafy greens.

The project also includes the creation of a relatively simple webpage that enables users to run different scenarios after entering different factors. The model will be most useful for produce groups or associations involved in developing policies, rules or best management practices.

The researchers explained that the development of a supply chain risk model involves identifying the stages of product production—which, for fresh produce, includes growing, harvesting, processing, retail, and consumer handling—and describing the stages mathematically. For their model, the researchers decided to focus on the stages where the produce industry has the most control, which excludes consumer handling.

During the first year of the project, the researchers investigated how common industry practices, like improved process wash and additional product testing, influenced the likelihood of positive tests for pathogens at retail and of removing lots with the highest potential contamination levels. As part of their analysis, they examined risk model sensitivity.

Once the researchers solidified a “backbone model,” they met with CPS Industry Advisory Council members and industry representatives to consider their input about what is needed to make good decisions, contamination concerns, and control strategies of interest, how impactful such deviations could be for food safety, and how aggressively one should respond. Industry engagement also included informal discussions with attendees at recent CPS Annual Symposiums.

During the project’s third year, the researchers are developing a risk model for a set of small-scale deviations like irrigation water treatment failure, incomplete harvester sanitation, small animal intrusion, and poor wash water control. They also hope to engage in industry discussions around managing Salmonella in melons. The two-step process involves mathematical computations based on current science and the likelihood of contamination. Once the math is figured out, the researchers must determine how to run it through a process model.