Advancing Honey Authentication and Traceability Using Fluorescence Excitation-Emission Spectroscopy Coupled with Machine Learning Molecular Fingerprint Analysis

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Our innovative project aims to combat the widespread issue of honey adulteration, which has become the second most prevalent form of food fraud globally, following only milk. This concerning problem undermines consumer trust, compromises the integrity of the honey industry, and poses potential health risks. Recent findings published in the EU report "From the Hives" revealed that nearly half of all honey imported into the EU contained markers of extraneous sugar sources, indicating adulteration. Furthermore, a 100% suspicion rate was found for honey imported from the UK, putting the reputation of British honey at risk.We will revolutionize honey quality control by introducing a fast and reliable method using FLuorescence Excitation-Emission (FLE) spectroscopy combined with machine learning. Current techniques, such as chromatography, NMR and sensory analysis, are expensive, time-consuming and becoming outdated. By applying FLE spectroscopy, an existing technology, in a new area of honey quality control integrated with machine learning, we take a disruptive approach to conventional methods.The project utilizes advanced machine learning techniques like Parallel Factor Analysis (PARAFAC) and Partial Least Squares Discriminant Analysis (PLS-DA) to analyse honey samples. These techniques enable us to determine the chemical components, ratios, and quality markers in the samples. By harnessing the speed, accuracy, and optical capabilities of FLE spectroscopy in combination with machine learning algorithms, our project aims to surpass current state-of-the-art methods for honey quality assessment. The result is faster, more reliable, reproducible, and cost-effective quality control methods that allow for third-party verification of product contents.Our consortium is a collaboration of experienced beekeepers and university scientists specialising in honey production, luminescent detection, and data analytics, we will leverage their collective expertise of over 25 years. Through this collaboration, we will develop innovative, efficient, and cost-effective testing methods that accurately detect honey adulteration.Implementing our proposed approach to honey quality control will not only set our product apart from competitors in the national and international testing and analysis services markets but also lead to the establishment of new standard kits for product quality and authenticity testing. This will enhance consumer confidence, increase product value, and ultimately contribute to protecting the reputation of British honey.