Researchers at Cornell University are helping food companies and their food safety managers get one step closer to preventing listeria contamination thanks to the technological advancements of what's known as a "digital twin."
The digital twin was created by replicating a fresh-cut produce facility. In this instance, the digital twin model was used to identify the optimal times and locations to look for the bacteria’s presence and therefore prevent food contamination.
According to a study published in the Journal of Applied and Environmental Microbiology, by using a digital twin, researchers were able to provide food safety managers a way to visualize microbial contamination risks and patterns in their operations without stopping production to experiment and test.
“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,” Isaid Renata Ivanek, Ph.D. ’08, associate professor at the College of Veterinary Medicine and senior author on the study.
The researchers plan to develop similar models for produce packing houses as well as grocery stores, thereby providing the food industry with digital twins that can be updated with real-time data, and can use simulation, modeling and machine learning to help workers make decisions about food safety hazards.