Honey authentication using intrinsic DNA markers and metabolic fingerprint

UK bee farmers are striving to produce their own monofloral honeys, with heather honey being the most important. However, research into the chemical composition and bioactivity of heather honey is sparse. In addition, the quality characteristics of heather honey are not well defined, making authentication difficult.
Another major challenge is adulteration. At present, there is no single method for authenticity testing for honey and the majority of the tests are time consuming.
Hypothesis: We hypothesise that intrinsic DNA markers and metabolic fingerprint/biomarkers in honey and floral sources can be paired with artificial intelligence to develop novel methods for simultaneous botanical origin identification and adulteration detection in UK heather honey.
The proposed research will:
1. Design a DNA markers/q-PCR study to characterise the botanical origin of UK heather honey using available sequence information to create species-specific oligos. A qualitative/quantitative method will be developed for common adulterant detection such as corn/rice syrup based on the same principles.
2. Use DNA markers to screen nectar and pollen from heather sites across the UK and the corresponding honey harvested from the same sites to ensure traceability.
3. Perform VOC and polar compound analysis with GC-MS and LC-MS respectively to characterise the metabolic profile of plant and honey samples from obj.2 and identify intrinsic biomarkers originating from the floral sources.
4. Develop a decision tool using machine learning algorithms to determine botanical origin of heather honey (monofloral, multifloral, not-heather) and detect adulteration, based on DNA and metabolic biomarkers.
5. Screen honey extracts for antimicrobial activity and bioactivity.
6. Develop sensor-based classification models using protocols already developed at CU, as an alternative low-cost method employable in field.