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Knowledge Centre for Food Fraud and Quality

The Knowledge Centre for Food Fraud and Quality (KC-FFQ) produces and makes sense of scientific information to protect the authenticity and quality of food in the EU

Methods for verifying honey authenticity

The page lists the main methods to detect adulteration of honey. The methods can either detect specific markers of adulteration, or try to identify adulteration via pattern analysis.

Honey adulteration, which involves the addition of known or unknown compounds, is not allowed.  To detect such adulteration, different methods can be used, broadly categorised in two groups: one set of methods focusses on known compounds, including markers inherent to honey (e.g., sugars, amino acids) and markers indicative of adulteration or contamination. Whereas the other set of methods generates a unique “fingerprint” of authentic honeys and checks whether an unknown sample falls into the variation of those fingerprints.

 

 

 

Markers for adulteration

Various substances have been suggested in the literature that should either not be present in honey at all, or present only at low level. While the substances themselves are usually not harmful, they may indicate adulteration. The advantage of these methods is that one knows what is measured and has therefore a good control over the evaluation. The disadvantage is that novel forms of adulteration may not be detected. In addition, fraudsters may change their adulterants to pass the tests.

 

Measurement methods for the general detection of adulteration

These methods do not aim to detect single substances, but look at the “fingerprint”, meaning a pattern of all substances detected in the honey. They then use pattern recognition to check if an unknown honey “looks similar” to the authentic ones. This way of analysing is called “non-targeted”, as it does not target specific substances, but the overall composition.

The advantage of this approach is that it does not require knowledge about the nature of adulteration or specific markers and so can potentially detect new and unknown ways of adulteration.

One disadvantage is that they rely on an extensive database of authentic samples to establish the pattern of an authentic honey. If a honey is rarely encountered (for example due to a different geographic or floral origin), it may be falsely flagged as adulterated.

A second disadvantage is that the system is a “black box”. The outcome of the analysis is a statement that a given sample is/is not sufficiently similar to authentic honeys. How this “sufficiently similar” is defined is a part of the mathematical data treatment.

Private laboratories and laboratory groups have built up extensive databases and offer testing for honey adulteration.