It is accepted that there is no “silver bullet” to check for honey authenticity. There are a plethora of test methods and analytical approaches, each of which can increase suspicion of sophisticated fraud. A weight of evidence approach is recommended, combining a range of analytical information with supply chain illumination, audit and traceability data. One recent review (open access) found an astonishing 386 patents and 707 published methods relating to honey authenticity testing. It can be difficult to judge which of these go beyond proof-of-concept to being a valid addition to a weight of evidence toolkit.
One approach which has generated much recent interest is Next Generation Sequencing (NGS). In principle, the DNA in honey (including meta-data from the microbiome) should be indicative of the local environment and flora where the bees foraged. There have been a number of proof-of-concept studies with researchers reporting high mislabelling rates based on their findings.
The EC Joint Research Centre have published a clear and accessible article in the popular science journal Nature - Of bees and buzz: towards validated NGS-based methods in honey authentication (open access link). This explains the principles of NGS (“meta-genomics”) methods, their great potential, but also the complexity inherent in robust validation and verification for widespread use testing real market samples. The source of DNA meta-data depends on many interlinked variables; floral seasonality, human activities such as agriculture and urbanisation, bees’ foraging behaviour variation over the seasons, physiological state of flora, the community diversity and the competition with pathogens or other pollinators. The environmental and pollen DNA found in a honey jar is derived from this dynamic interplay, leading to a DNA composition changing during a season rather than remaining static. Add to this the fact that honey from different hives are legally mixed, filtered and processed and the validation required to underpin classification models becomes incredibly complex.
The authors recommend a coordinated strategy to realise the potential of metagenomic classification methods. This begins with the creation of a dedicated honey‑DNA reference library. This library should be assembled from a large, well‑documented set of authentic honeys that captures the full spectrum of botanical origins, geographic regions, seasons and the typical blends found in commercial products. Each sample must be unquestionably authentic, either by sourcing from certified producers or by confirming authenticity with orthogonal methods. They argue that existing databases such as BEEexact and BeeRoLaMa demonstrate feasibility of shared and curated databases, but there needs to be comparability evidence of different laboratory workflows. They give recommendations for achieving this, and conclude that this would enable reliable discrimination between natural variability and intentional adulteration.
Image from the JRC paper
