A group of researchers are developing a risk-based model for customized produce sampling programs that take variables such as field size and commodity being tested into account.
Matthew Stasiewicz, assistant professor of applied food safety at the University of Illinois-Urbana-Champaign are working on the sampling model to give growers plans that have the highest potential for detecting foodborne pathogens.
The Center for Produce Safety funded the $250,000 year-long study.
“Our goal is to ultimately help growers do a better job of in-field testing for foodborne pathogens, so the growers can continue to improve on the safety of their produce,” Stasiewicz said in a CPS news release. “Risk-based sampling is something we know the industry is trying to move toward.”
If growers know where high-risk areas are in the fields, they can have more stringent sampling in those areas, according to Stasiewicz.
Common methods of sampling have pre-set areas where samples are taking, such as every 100 yards, or in a “Z” pattern, according to the release. But that doesn’t take into account places that have a higher risk of contamination, such as under power lines where birds might gather, according to the release.
The research is backed by a grower who is a large data set from its own sampling.
“One of the things we’re trying to do is use real produce industry data to review the structure of our model to ensure it gives valid results,” Martin Wiedmann, food safety professor at Cornell University, said in the release. “As we build our model, we will use results from our industry partner’s operations.”
Wiedmann, the co-principal investigator of the study, is helping determine field risks and experimental tools to collect that data.
“Once we know where their n60 samplings (many food safety programs take 60 samples from a field) were taken, we will compare their positive and negative samples to our computer simulations of similar fields,” Wiedmann said in the release. “This comparison will help us ensure we are simulating fields that represent real practices. It’s really important that we interact with the produce industry to keep getting that continual reality check.”
Simulation models representing fields in California’s Central Valley, Yuma, Ariz., the Delmarva Peninsula and upstate New York will be used to simulate one of three types of contamination:
- Point source, such as animal feces;
- Systemic sources such as contaminated irrigation water; or
- Sporadic contamination, such as low-level endemic soil bacteria.
Researchers will validate field simulations by infecting a Cornell University spinach field with an “indicator organism,” take 450 samples and compare the results to the computer model, according to the release.
For more information on the study, see the CPS website.
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