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A new UK protocol can help make honey authenticity assessments more reliable by improving the reference samples used to build comparison databases. It is not a test for adulteration and cannot determine whether a particular jar is genuine: laboratories still need analytical methods to examine unknown honey.
What the protocol does—and does not do
The Government Chemist-led Protocol for the Collection of Honey Reference Samples for the Construction of Authenticity Databases standardises how samples are collected, documented, handled and stored when they are intended to form a database baseline. The work was co-funded by Defra and the Government Chemist, with a working group chaired by Selvarani Elahi MBE and involving stakeholder experts. It was published in October 2023 and updated in September 2024 to add online forms in Annex 4.
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Authenticity databases contain information about honey samples considered authentic within a defined scope. A laboratory can compare an unknown sample’s analytical results with that reference information. Better-documented, more representative reference samples can make those comparisons more meaningful, but the database does not replace the laboratory analysis.
The protocol explicitly excludes samples taken directly for authenticity testing, as well as non-honey reference materials such as syrups and bee-feeding products. It is a collection standard for building reference databases—not a consumer test, a method for detecting adulteration, or evidence that any individual honey is authentic.
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Why the reference database’s scope matters
Honey varies with geographic origin, floral source, season and year. A database can support conclusions only to the extent that its reference samples represent the honey types and supply-chain products the database is meant to assess. A baseline built only from UK-produced honey, for example, cannot by itself justify conclusions about honey from other origins.
The protocol advises planners to define their intended use before collecting samples. If a database is meant to assess retail products, it should include representative retail products or establish a traceable link between other samples and those products. Import and sales data can help structure sampling, and the plan should consider statistically relevant sample numbers. Comparison with honey produced in the same year can increase confidence because composition changes over time.
This is especially relevant to blended products. The protocol cites A C Nielsen data indicating that blended honey accounted for 95% by volume of the UK retail market for the 12 months ending 31 December 2022. That is a historical figure reported in the protocol, not a current market-share estimate. For a retail-focused database, records about processing, blending and the provenance of blend components are therefore important.
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How samples are collected along the supply chain
The protocol covers collection points from apiaries and beekeepers through processing, import-country handling and retail products. Its Annex 4 forms are tailored to different points, including beekeeper or batch information, processing and load details, import-country sampling, and the final honey lot. Choosing the collection point should follow the database’s purpose: a sample taken earlier in the chain may need a documented link to the product or blend it is meant to represent.
For bulk honey, the protocol regards collection by an independent organisation as the strongest of its listed approaches for demonstrating traceability. It calls for trained personnel and a predefined, documented procedure. Records may range from complete digital logs linked to time, date and GPS to paper or other electronic records; the level of confidence conveyed in later reporting should reflect how complete the records are.
Bulk honey in drums or other containers
For non-homogenised honey held in drums or similar bulk containers, the protocol recommends a hollow sampling spike, commonly called a corer, often with an integral plunger. The goal is to collect a suitable sample from material that has not been evenly mixed, while following a consistent documented procedure.
Homogenised honey in multiple units
Where honey is homogenised and held in many units, the protocol describes using recognised statistical approaches to select units and combining equal portions of subsamples. It recommends at least 100 g for each subsample and at least 300 g for a single sample or aggregate sample.
Keeping identity and sample integrity intact
Each sample needs a unique identification code or number and a record linking it to relevant provenance information across the supply chain. The protocol recommends clean, food-grade, opaque containers made of glass or, preferably, plastic. Seal the container—or place it in a bag and seal that—with a tamper-evident closure.
Records can include beekeeper and location information, collection details and, where appropriate, supporting analytical evidence. Pollen and sensory analysis may help establish floral origin; established quality tests may also be relevant. The protocol recommends that analyses ideally be performed by laboratories using methods accredited to ISO 17025.
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Transport, storage and laboratory preparation
The protocol recommends transport and storage at approximately 25–35°C before analysis to avoid crystallisation and maintain honey quality. For samples with moisture above 23.0%, it recommends frozen storage from collection to analysis. After testing, it strongly recommends storage at −18°C or lower. If a sample may be tested again, keep a record of its storage history.
Crystallised samples should be avoided at the laboratory. If crystals are observed, the protocol describes gentle warming to no more than 40°C followed by thorough mixing. These handling measures preserve sample condition; they do not reveal whether honey has been adulterated.
What current analytical research can establish
The Food Standards Agency’s 2025 report says no single method currently tests all aspects of honey authenticity. The report investigated spatially offset Raman spectroscopy (SORS) with machine-learning models for rice- and sugar-beet-syrup adulteration, and DNA markers using qPCR for evidence about plant sources. These methods address particular questions, not every possible adulterant or authenticity claim.
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In that study, researchers examined 17 UK honey types and simulated adulteration levels of 1–30% for qPCR and 10–50% for SORS. The DNA method distinguished adulterated samples at 1%. For the SORS random-forest model, the report gave misclassification rates of 1.1% of pure samples classified as adulterated and 3.64% of adulterated samples classified as pure. Those are results from the study’s samples and models, not universal performance guarantees; the authors called for model refinement, fewer misclassifications and broader coverage of commercial honey.
In a separate US context, the Food and Drug Administration’s FY2025 assignment tested 54 domestic and 48 imported honey samples and found two violative samples in each group—four of 102 total. The FDA cautions that the assignment was not designed to compare violation rates by country of origin. It also says its current method detects only a subset of undeclared sweeteners and that further methods are under development. Those results do not evaluate the UK sampling protocol.
Validation guidance from AOAC reinforces why database design matters: the scope of a nontargeted honey-testing method depends on the authentic samples used to establish its baseline fingerprint, and validation samples should be independent and cover the method’s intended scope. A classification therefore has meaning only in relation to the reference data and validation behind it.
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What this means for consumers and laboratories
For consumers, the protocol does not offer a way to check a jar at home. Its benefit is upstream: consistent collection and provenance records can help laboratories and database developers build stronger comparison baselines. For laboratories and database builders, the central practical questions are whether the reference set matches the intended products and origins, whether each sample’s identity and handling are documented, and whether the analytical method has been validated for the specific adulterants and honey types at issue.
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