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Quickly identifying the source is key to stopping outbreaks of foodborne illness
According to reporting from Farms, a team of scientists led by researchers at the University of Georgia Centre for Food Safety in Griffin has developed a machine-learning approach that could lead to quicker identification of the animal source of certain Salmonella outbreaks.
In the research, published in the January 2019 issue of Emerging Infectious Diseases, Xiangyu Deng and his colleagues used more than a thousand genomes to predict the animal sources, especially livestock, of Salmonella Typhimurium.
Deng, an assistant professor of food microbiology at the centre, and Shaokang Zhang, a postdoctoral associate with the centre, led the project, which also included experts from the Centres for Disease Control and Prevention, the US Food and Drug Administration, the Minnesota Department of Health and the Translational Genomics Research Institute.
According to the Foodborne Disease Outbreak Surveillance System, close to 3,000 outbreaks of foodborne illness were reported in the U.S. from 2009 to 2015. Of those, 900 — or 30 percent — were caused by different serotypes of Salmonella, including Typhimurium, Deng said.
“We had at least three outbreaks of Typhimuirum, or its close variant, in 2018. These outbreaks were linked to chicken, chicken salad and dried coconut,” he said. “There are more than 2,600 serotypes of Salmonella, and Typhimurium is just one of them, but since the 1960s, about a quarter of Salmonella isolates linked to outbreaks reported to U.S. national surveillance are Typhimurium.”
The researchers trained the “machine,” an algorithm called Random Forest, with more than 1,300 S. Typhimurium genomes with known sources. After the training, the “machine” learned how to predict certain animal sources of S. Typhimurium genomes.
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