CSU team receives multi-year USDA grant to improve modeling of contamination
The Centers for Disease Control and Prevention estimates that each year food borne diseases lead to over 128,000 hospitalizations and roughly 3,000 deaths. Many of these illnesses can be traced to the presence of pathogens such as E. coli, Salmonella, and Listeria in fresh produce. Although interventional practices, including highly engineered and monitored washing systems, have been developed to limit public exposure to these pathogens, mortality and illness rates remain stubbornly high.
A multi-disciplinary team at Cleveland State University has been awarded a 3-year, $258,000 grant from the U.S. Department of Agriculture to address this significant public health concern. The team will develop a new mathematical model designed to better assess how these pathogens spread through the food supply and ultimately create better controls to reduce contamination.
“Ironically, the produce washing process has been identified as a key point of pathogen transfer in the food supply chain as contaminated produce mixes with uncontaminated food and pathogens ‘escape’ without being completely removed from all items," says Partha Srinivasan, associate professor of mathematics at CSU and principal investigator on the project. “Our team of mathematicians and chemical engineers will seek to better analyze the chemical process to reduce the risk of pathogen transfer while also creating better predictive modeling for how the entire process impacts food safety.”
Previous models designed to assess how this contamination occurs have only taken a “snap shot” in time instead of modeling the entire wash system and all of its inputs, including food intake and outtake, material handling and sanitization and rinse. This has made predictive analysis difficult at best. The team, which also includes Dan Munther, assistant professor of mathematics, and Chandra Kothapalli, associate professor of chemical engineering, both at CSU, will address this by creating a fully operational produce washing system in a laboratory environment that will enable better prediction of pathogen cross-contamination and chemical analysis of sanitizer performance.
The goal will be to provide the food industry with better data on how wash systems can be improved to reduce cross contamination and make cleaning systems more effective without increasing chemical use.
“This really is a balancing act because just increasing use of sanitizer or adding stronger chemicals could also have public health risks,” adds Munther. “Our hope is to provide better information that will make washing systems and the food supply chain overall safer and more effective in protecting public health.”