From Bee Or Not From Bee

Some Background

Sagitto first became involved with honey analysis in 2015, when we applied machine learning to metabolomic data to create a set of unique biochemical ‘fingerprint’ markers to clearly distinguish nectars and honeys by floral type¹.

The explosive interest in New Zealand manuka honey as a 'superfood' has led to very high prices for genuine manuka honey (as shown in this graph). However high price differentials for mono-floral honey also increases the economic incentive for adulteration - not only with cheaper honeys, but also with non-honey syrup substitutes such as corn and rice syrup. In response to this threat, over the past ten years a number of different analytical techniques have been developed for determining the floral origin of honey, and its potential adulteration. This blog post will discuss some of these.

Testing Honey Using NIR Spectroscopy

We first wanted to see whether NIR spectroscopy could be used detect honey adulteration. We obtained 12 New Zealand honey samples of known providence, and  scanned each with a Sagitto miniature NIR instrument to obtain its NIR absorbance spectrum.

We purchased corn syrup and brown rice malt syrup from a local supermarket, and mixed varying quantities with approximately 15gm of individual honeys to emulate adulteration of honey with cheaper syrups.

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After scanning various mixtures of honey and syrup, we created two regression models - for clover honey adulterated with corn syrup, and mutlifloral honey adulterated with rice syrup.

These two regression models looked very promising. However when we generalised our model to include adulteration of other honeys, we noticed that the model tended to predict the presence of adulterant even in pure honeys (as circled in the lot below.)

A classification model, that sought to distinguish between pure and adulterated honeys, showed a similar pattern of predicting false positives. Almost all adulterated honeys were correctly predicted, but 7 out of 12 pure honeys were incorrectly classed as adulterated. This result is consistent with a study² done in 2014/15 by PerkinElmer using FT-NIR, and highlights a limitation in using NIR for the purposes of detecting honey adulteration.

C4 Sugars Screening

(to be completed)

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Acknowledgements

Almost all honey used in this study was from two reputable and long-established family companies - Generation Honey and Airborne Honey. This table lists these honeys, together with their Brix and colour measurements.

References

1. Unpublished research conducted in conjunction with the University of Waikato and Analyical Laboratories Ltd.
2. Detection of Honey Adulteration Using FT-NIR Spectroscopy
3. ‍C4 Sugars Screening Test
4. Application of Spatial Offset Raman Spectroscopy (SORS) and Machine Learning for Sugar Syrup Adulteration Detection in UK Honey
5. Differentiation and classification of Australian single botanical origin honeys using spatially offset Raman spectroscopy
6. Example NMR Honey Profiling Report

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