Could AI prevent food poisoning?
An international team of researchers has demonstrated how artificial intelligence (AI) can now detect contaminated food in fields and factories before it reaches consumers, potentially preventing four million deaths annually.
Led by the University of South Australia (UniSA), the team’s study published in the journal Toxins describes how advanced hyperspectral imaging (HSI) integrated with machine learning (ML) can identify mycotoxins — dangerous compounds produced by fungi that can contaminate food during growth, harvest and storage.
Mycotoxins cause a range of serious health issues, such as cancer, compromised immunity and hormone-related disorders. According to the World Health Organization, foodborne contamination, including from mycotoxins, results in 600 million illnesses and 4.2 million deaths each year.
The UN-based Food and Agricultural Organization estimates that about 25% of the world’s crops are contaminated by mycotoxin-producing fungi, highlighting the economic and health imperatives to address this threat. Lead author and UniSA PhD candidate Ahasan Kabir meanwhile noted that traditional mycotoxin detection methods are time-consuming, expensive and destructive, making them unsuitable for large-scale, real-time food processing.
“In contrast, hyperspectral imaging — a technique that captures images with detailed spectral information — allows us to quickly detect and quantify contamination across entire food samples without destroying them,” Kabir said.
Kabir and his co-authors in Australia, Canada and India evaluated the effectiveness of HSI in detecting toxic compounds in cereal grains and nuts. Both are highly susceptible to fungi and mycotoxin contamination in warm, humid environments, from cultivation to storage.
“HSI captures an optical footprint of mycotoxins and when paired with machine learning algorithms it rapidly classifies contaminated grains and nuts based on subtle spectral variations,” Kabir said.
The researchers reviewed more than 80 recent studies across wheat, corn, barley, oats, almonds, peanuts and pistachios. Their findings showed that ML-integrated HSI systems consistently outperformed conventional techniques in detecting key mycotoxins.
“This technology is particularly effective at identifying aflatoxin B1, one of the most carcinogenic substances found in food,” said project lead UniSA Professor Sang-Heon Lee. “It offers a scalable, non-invasive solution for industrial food safety, from sorting almonds to inspecting wheat and maize shipments.”
The researchers say that, with further development, HSI and ML could be deployed on processing lines or handheld devices, reducing health risks and trade losses by ensuring that only safe, uncontaminated produce reaches consumers. The team is now working on refining the technique to improve its accuracy and reliability, using deep learning and AI.
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