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PassTrac Analytics helps meat firms enhance food safety programs

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By Joe Whitworth+



PassTrac Analytics has partnered with a company that uses predictive modeling and prescriptive analytics technology to enhance the food safety programs of meat protein companies.

The division of Passport Food Safety Solutions will work with PurePredictive.

PassTrac Analytics said it can help packers and processors reduce presence of pathogenic bacteria such as Shiga toxin-producing E. coli and Salmonella.

Microbial data is received from the plant and PassTrac Analytics enhances it with relevant operational and environmental data.

Then, it uses PurePredictive’s technology to find patterns that would go undetected by a data expert using traditional statistical techniques.

Insights from these models identify opportunities to optimize food safety programs, to reduce risk and enhance profitability for the plant.

John Ruby, VP of PassTrac Analytics, said managing food safety systems is increasingly complex.

“Historical methods to track and analyze traditional food safety indicators are reaching their limitations. This innovative technology from PurePredictive enables PassTrac to take analytics well-beyond anything currently being done in the food safety industry,” he said.

“We can provide critical insights that meat protein companies need in order to make the most proactive and cost-efficient decisions possible.”

Trajan Bayly, CEO at PurePredictive, said the patented predictive modeling technology provides precision with greater speed.

“While it has been applied effectively in other industries, food safety presents a unique opportunity to make a momentous difference,” he said.

“By adding PurePredictive’s innovative technology to PassTrac Analytics’ deep industry expertise and powerful analytical capabilities, Passport Food Safety Solutions will be able to help meat protein companies not only reduce their risk, but proactively reduce foodborne pathogens more effectively and economically than the industry has been able to do before now.”

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