AI used to enhance milk safety testing and detect food fraud

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 unauthorised 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, 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 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-pasteurisation, post-pasteurisation, or is 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. This study characterised raw milk metagenomes — collections of genomes from many individual microbes within a sample — in what is claimed to be more sequencing depth than any other published work to date, and demonstrated that there is a set of consensus microbes found to be stable elements across samples.

The study’s findings suggest that AI has the potential to enhance the detection of anomalies in food production, providing a more comprehensive method that can be added to scientists’ toolkit for ensuring food safety, Ganda explained.

“Traditional analysis of microbial sequencing data, such as alpha and beta diversity metrics and clustering, were not as effective in differentiating between baseline and anomalous samples,” she said. “However, the integration of AI allowed for accurate classification and identification of microbial drivers associated with anomalies.”

Microbial systems and the food supply chain are an ideal application for AI since the interactions between microbes are complex and dynamic, according to the study’s first author Kristen Beck, senior research scientist from IBM Research.

“There are also a multitude of variables in the food supply chain that affect the signal we’re seeking to observe,” she said. “AI can help us untangle the signal from the noise.”

While this research focused on dairy production, the findings have implications for the wider food industry. Issues in food quality and safety can have rippling effects through the supply chain, so there is substantial interest in applying both targeted and untargeted methods to identify ingredients or food products that show an increased risk of food fraud, food quality and food safety issues.

“Untargeted methods characterise all molecules that can be identified to identify ingredients or products that deviate from a ‘baseline state’ that would be considered normal or under control,” she said. “Importantly, these untargeted methods are screening methods that do not define an ingredient or product as unsafe or adulterated; rather, they suggest an aberration from the normal state that should trigger follow-up actions or investigations.”

The research collaboration featured all three partners as follows:

• IBM’s open-source AI technology, Automated Explainable AI for Omics, was used to process vast amounts of metagenomic data, or all the nucleotide sequences isolated and analysed from all the microbes in bulk milk samples, enabling the identification of microbial signatures that traditional methods often can miss.
• The Cornell researchers’ expertise in dairy science elevated the practical relevance of the research and its applicability to the dairy industry.
• Penn State’s One Health Microbiome Center in the Huck Institutes for the Life Sciences played a critical role in integrating microbial data for broader health and safety applications.

Image credit: iStock.com/Mercedes Rancaño Otero