Preventing Listeria Outbreaks: New Study Develops 'Digital Twin' for Fresh-Cut Produce Facilities

Food safety managers have a new tool to help prevent Listeria outbreaks in fresh-cut produce facilities, thanks to a recent study by researchers from Cornell University. The study developed a 'digital twin' of two such facilities, using digital models to identify the optimal times and locations to look for the bacteria's presence and thereby prevent food contamination.

Listeria monocytogenes, the bacterium responsible for Listeriosis, can cause severe illness if certain foods aren't pasteurized, cooked thoroughly enough or washed properly. In fact, it is estimated that Listeriosis causes approximately 260 deaths and 1,600 infections each year. Food producers must take proactive steps to prevent these outbreaks, and the new study provides a novel approach for doing so.

The researchers' model allows food safety managers to first visualize microbial contamination risks and patterns in their operations, and then experiment with different environmental sampling practices. For example, they can collect sponge samples from different pieces of equipment to determine where Listeria is most likely to be present. This information can help facility operators take targeted actions to prevent contamination.

The study's senior author, Renata Ivanek, Ph.D., an associate professor at the College of Veterinary Medicine, noted that 'our findings are another step forward in equipping food producers with science-based tools to manage food safety risks.' The researchers believe that their digital twin approach can be used not only for fresh-cut produce facilities but also for other food processing and manufacturing settings.

Overall, the new study highlights the importance of using data-driven approaches to prevent Listeria outbreaks. By leveraging computer programming and food science expertise, the research team has developed a powerful tool that can help the food industry better protect public health.