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Managing Listeria risk in fresh produce using predictive models

Schaffner, Donald
Rutgers University
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Many foods are perishable and require time/temperature control throughout their shelf life. In many cases this control is required to ensure quality, but in an uncertain number of situations, it may also be required for food safety (to control foodborne pathogen growth). This issue is often discussed by fresh produce producers and buyers and is now exacerbated by several federal regulations and policies including the Preventive Controls Rule and Sanitary Transportation Rule. While research funded by CPS may eventually provide definitive laboratory-based information to guide these discussions, there is an urgent need for short-term science-based parameters on this topic. This project will focus on the pathogen most likely to grow at the temperature range of interest (Listeria monocytogenes) and will use "off-the-shelf" computer models in the form of ComBase Predictor ( The objective will be to produce a report with a series of time and temperature tables, comparing relative risk of Listeria monocytogenes growth for different conditions to guide science-based risk management decisions.
Project source
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Bacterial Pathogens
Natural Toxins
Risk Assessment, Management, and Communication