Researchers have developed an innovative method using artificial intelligence (AI) and genetic sequencing to improve dairy safety by detecting contamination or unauthorized additives in milk.
This groundbreaking approach, spearheaded by scientists from Penn State, Cornell University, and IBM Research, could revolutionize how anomalies in food production are identified.
According to findings published in the *mSystems* journal, the team analyzed microbes in raw milk using AI algorithms and metagenomics.
They successfully detected milk that had been experimentally treated with antibiotics.
This method, which analyzes the genetic sequences of all microbes in a sample, identified microbial signatures linked to anomalies, providing a more accurate assessment than traditional techniques.
Genetic sequencing combined with AI identifies microbial anomalies, safeguarding milk from potential risks.Erika Ganda, assistant professor at Penn State, explained that the AI tool revealed microbial characteristics, allowing researchers to distinguish milk from different sources or production stages.
Their study of 58 bulk tank milk samples showed that AI can differentiate between normal and potentially contaminated samples.
Kristen Beck from IBM Research emphasized the complexity of microbial systems, stating that AI’s ability to identify subtle signals among numerous variables makes it an ideal tool for food safety.
The team’s work demonstrates that AI can be a powerful tool in ensuring food quality across the supply chain.
Though the study focused on dairy, its implications extend to the broader food industry.
Food fraud and safety issues can have severe economic and health consequences, and this untargeted method offers a promising solution for identifying ingredients that deviate from expected standards.
Researchers believe this technique could be a significant step forward in safeguarding food production.