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

In findings published in mSystems 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 the study's lead Erika Ganda, assistant professor of food animal microbiomes, Penn State 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 pre-pasteurization, post-pasteurization, or is from a cow that has been treated with antibiotics."

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