An AI Boost to Milk Safety

By combining genetic sequencing and analysis of the microbes in a milk sample with artificial intelligence (AI), researchers could detect anomalies such as contamination or unauthorized additives. Study authors from Penn State, Cornell University and IBM Research say the new approach could help improve dairy safety.

In findings published in mSystems, a journal of the American Society for Microbiology, the researchers reported that using shotgun metagenomics data and AI, they were able to detect antibiotic-treated milk that had been experimentally and randomly added to the bulk-tank milk samples they collected. To validate their findings, the researchers also applied their explainable AI tool to publicly available, genetically sequenced datasets from bulk milk samples, further demonstrating the untargeted approach's robustness.

"This was a proof-of-concept study," said lead author Erika Ganda, assistant professor of food animal microbiomes in the College of Agricultural Sciences. "We can look at the data from the microbes in the raw milk and, using artificial intelligence, see if the microbes that are present reveal characteristics, such as whether it is prepasteurization, postpasteurization or from a cow that has been treated with antibiotics."

The researchers collected 58 bulk-tank milk samples and applied various AI algorithms to differentiate between baseline samples and those representing potential anomalies, such as milk from an outside farm or milk containing antibiotics. The study's findings suggest that AI has the potential to enhance detection of anomalies in food production, providing a more comprehensive method that can be added to scientists' toolkit for food safety, Ganda explained.

—Jeff Mulhollem