IBM and Cornell University, which primarily focuses on dairy research, will make use of artificial intelligence (AI) to make dairy safe for consumption.
According to the U.S. Department of Agriculture, Americans consume more than 600 pounds of milk and milk-based products per person per year.
Fresh food such as meat, dairy, and produce represent a great risk for food safety incidents, as they are tested for a few specific groups of bacteria.
By sequencing and analyzing the DNA and RNA (genetic code) of food microbiomes, researchers plan to create new tools that can help monitor raw milk to detect anomalies that represent food safety hazards and possible fraud.
While many food producers already have rigorous processes in place to ensure food safety hazards are managed appropriately, this pioneering application of genomics will be designed to enable a deeper understanding and characterization of microorganisms on a much larger scale than has previously been possible.
"Through this partnership with Cornell University, we are extending the consortium work to a broader range of ingredients, leveraging artificial intelligence and machine learning, to gain new insights into how microorganisms interact within a particular environment," Jeff Welser, vice president and director of IBM Research, Almaden, said, according to a report by Genome Web.
The Consortium for Sequencing the Food Supply Chain was officially launched in January 2015 by IBM Research and Mars. Bio-Rad Laboratories, a global provider of life science research and clinical diagnostic products, joined the Consortium in 2016. This collaborative food safety initiative will leverage advances in next-generation sequencing to further the understanding of what can help make food safe.
This work could eventually be extended to the larger context of the food supply chain — from farm to fork — and, using artificial intelligence and machine learning, may lead to new insights into how microorganisms interact within a particular environment.