Collaboration set to develop tools to defeat honey fraud

With support from the British Beekeepers Association and the Honey Authenticity Network UK, they are developing technology to detect if honey has been blended with ingredients including sugar syrup.

Globally honey adulteration is the second most common food fraud, following milk. According to Iain Millar, director of The Scottish Bee Company, honey fraud has far reaching implications for food security, land use and biodiversity. 

Earlier this year the EU study From the Hives revealed the extent of honey fraud from 320 samples. It found that 10 samples of honey exported to the EU from the UK were all suspected of being adulterated. Although they have been blended or packaged in Britain, the honey might have originated overseas, which was likely the result of honey produced in other countries and further blended in the UK before its re-export to the EU. Overall, 46 per cent of the samples in the study were found to be adulterated. 

Researchers at the Aston Institute for Photonic Technolgies (AIPT) and The Scottish Bee Company have been awarded an artificial intelligence feasibility study grant from Innovate UK to develop honey authenticity testing technology.

Honey samples will be examined using FLuorescence Excitation-Emission (FLE) spectroscopy combined with machine learning to create a fast and reliable testing method. Current techniques, such as chromatography, nuclear magnetic resonance and sensory analysis are expensive and time-consuming.

Research lead Dr Alex Rozhin, reader in nanotechnology within Aston Institute of Photonic Technologies (AIPT), said the project aims to enhance consumer confidence, elevate product value, and safeguard the reputation of British honey.

“The team will utilise advanced machine learning techniques, specifically parallel factor analysis [PARAFAC] and partial least squares discriminant analysis [PLS-DA], to analyse honey samples,” he said in a statement. “These techniques offer the capability to identify the chemical constituents, assess their ratios, and determine quality markers within the samples.

“By combining the rapidity, precision, and optical capabilities of FLE spectroscopy with machine learning algorithms, the project aims to surpass existing… methods for assessing the quality of honey.”

The research will take place at Aston University and will involve team members Dr Raghavan Chinnambedu-Murugesan, Dr Steve Daniels and Dr Valentina Barker.

The current project is expected to continue for six month and to translate into a UK-wide project that will create a comprehensive database of UK honey samples and develop portable instruments for honey sampling and detection.