Application of Spatial Offset Raman Spectroscopy (SORS) and Machine Learning for Sugar Syrup Adulteration Detection in UK Honey

Honey authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. The aim of this study was to develop non-invasive sensor methods coupled with multivariate data analysis for de-tecting the type and percentage of exogenous sugar adulteration in UK honeys. For this purpose, we employed through-container Spatial Offset Raman Spectroscopy (SORS) on 17 different types of natural honeys produced in the UK over the course of a season and the same honey samples spiked with rice and sugar beet syrups at levels 10%, 20%, 30%, 50% w/w. The data acquired were used to construct prediction models for 14 types of honey with similar Raman fingerprint using different algorithms, namely PLS-DA, XGBoost and Random Forest with the aim to detect the level of adulteration per type of sugar syrup. The best performing algorithm for classification was Random Forest with only 1% of the pure honeys misclassified as adulterated and < 3.5% of adulterated honey samples misclassified as pure. Random Forest was further employed to create a classification model which successfully classified samples according to the type of adulterant (rice or sugar beet) and the adulteration level. In addition, we collected SORS spectra from 27 samples of heather honey (24 Calluna vulgaris and 3 Erica Cinerea) produced in the UK and cor-responding subsamples spiked with high fructose sugar cane syrup and performed exploratory data analysis with PCA and classification with Random Forest which both showed a clear sepa-ration between pure and adulterated samples at medium (40%) and high (60%) adulteration levels and a 90% success at low adulteration levels (20%). The results of this study demonstrate the potential of SORS in combination with machine learning to be applied for the authentication of honey samples and the detection of exogenous sugars in the form of sugar syrups. A major advantage of the SORS technique is that it is a rapid, non-invasive method deployable in field with potential application at all stages of the supply chain.