Cornell researchers have developed a “digital twin” of two fresh-cut produce facilities to help the food industry prevent Listeria outbreaks. The researchers combined food science expertise and computer programming to create digital models of the facilities, which they used to identify optimal times and locations to look for the presence of Listeria monocytogenes and prevent food contamination. The model allows food safety managers to visualise microbial contamination risks and patterns in their operations and experiment with different environmental sampling practices. The study was published in the Journal of Applied and Environmental Microbiology and offers food producers science-based tools to manage food safety risks.
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Listeriosis, an infection caused by eating food contaminated by the bacterium Listeria monocytogenes, causes approximately 260 deaths and 1,600 infections each year. If certain foods aren’t pasteurized, cooked thoroughly enough or washed properly, the bacteria can take hold and cause severe illness, including brain infections.
Researchers from Cornell are blending food science expertise and computer programming savvy to help the food industry stop Listeria outbreaks. In a new study, the researchers developed a “digital twin” of two fresh-cut produce facilities, using these digital models to identify the optimal times and locations to look for the bacteria’s presence and therefore prevent food contamination.
“Our findings are another step forward in equipping food producers with science-based tools to manage food safety risks,” said Renata Ivanek, Ph.D. ’08, associate professor at the College of Veterinary Medicine and senior author on the paper.
The researchers’ model provides a novel way for food safety managers to first visualize microbial contamination risks and patterns in their operations, and then to experiment with different environmental sampling practices, such as collecting sponge samples from different pieces of equipment.
Because of the complexity of these facilities, experimenting in the actual environment is not always practical, and by using a digital twin, each facility can personalize its unique features. “For example, in the two facilities we modeled in this study, we wanted to find when sampling certain types of locations would be more beneficial than sampling random locations, and vice versa,” Ivanek said.
The study was published Oct. 14 in the Journal of Applied and Environmental Microbiology. Co-authors include Genevieve Sullivan ’16, Ph.D. ’20; Martin Wiedmann, Ph.D. ’97, the Gellert…
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