AI and mass spectrometry used to combat honey fraud

Stéphane Bayen, Associate Professor and Chair of McGill’s Department of Food Science and Agricultural Chemistry, said that honey is one of the most fraud-prone commodities in global trade.

“It often involves mislabelling where it was produced or the types of flowers that bees collected nectar from,” he said in a statement.

Honeys made from a single flower are often more expensive, prompting some producers to intentionally mislabel honey in order to charge more. Others may do so unknowingly, given that tracking exactly where bees collect nectar can be challenging.

The new method can determine what type of flowers the bees visited to produce a particular honey.

Until now, authenticating honey has been done through pollen analysis, a technique that fails after honey is processed or filtered. The new approach uses high-resolution mass spectrometry to scan honey at a molecular level to create a unique chemical fingerprint.

Machine learning algorithms then read the fingerprint to verify the honey’s origin. To check the accuracy of their method, the researchers tested it on a variety of honey samples and compared the results to honey from known botanical sources. 

“Right now, identifying the true source of honey can take days. With our method, we can do it in minutes, even for processed honeys where traditional techniques fall short,” said Bayen.

The scientists said demand for local varieties, like Quebec's blueberry honey, is rising as more people prioritise buying local. The new technique could serve as a safeguard for both consumers and ethical beekeepers.

“People deserve to know that their honey is what it claims to be, and honest producers deserve protection from fraudulent competitors,” said Bayen.

The researchers hope to see their technique adopted by food inspection agencies worldwide. Their next step is to explore how it could be used for other food products prone to mislabelling.

The team’s research is detailed in Analytical Chemistry. It was supported by the Agilent Thought Leader Award, the Canada Foundation for Innovation and the Natural Sciences and Engineering Research Council of Canada Alliance Project.