ARLINGTON, VA. – Listeria monocytogenes is an insidious and hardy pathogen that can survive in extreme temperatures and hard-to-reach places. But a new tool developed by researchers at Cornell University may help food manufacturers expose areas of food manufacturing facilities that are vulnerable to Lm contamination.
Findings published in the Jan. 24, 2019, issue of Scientific Reports state that researchers developed and tested a computer model named Environmental monitoring with an Agent-Based Model of Listeria (EnABLe) that can potentially pinpoint locations in a food manufacturing plant where Lm might be found. The research was funded in part by the Frozen Food Foundation which is affiliated with the American Frozen Food Institute.
Researchers entered data such as historical perspectives, expert feedback, details of food manufacturing equipment used and its cleaning schedules, in addition to the job functions and movement of materials and people within and from outside the facility. The researchers said insights gained from the discovery of patterns in the where Listeria spp. is predicted can inform the design of any food manufacturing facility and Lm-monitoring programs
“The goal is to build a decision-support tool for control of any pathogen in any complex environment,” said Renata Ivanek, Cornell University associate professor in the Dept. of Population Medicine and Diagnostic Sciences and senior author of the paper. “While a single person could never keep track of all this information, EnABLe connects data and potential sources of Lm contamination with approaches for risk mitigation and management.”
Tools like EnABLe help the frozen foods industry develop specific food safety protocols based on a better understanding of potential entry points for Listeria in frozen food facilities, noted Dr. Donna Garren, executive vice-president of the Frozen Food Foundation.
“Lm is a challenge because of its ubiquity and ability to survive freezing temperatures,” Garren said. “Cornell’s innovative work opens a new, predictive model for the frozen food industry to better understand and develop more robust food safety programs for detecting and minimizing the presence of Lm.”
The study will continue into 2019. Next steps include the application and testing of the model in select frozen food facilities in a potential industry wide roll out.