Abstract: In order to qualitatively and quantitatively identify syrup adulteration in honey, a method for rapid identification of adulterated honey by Raman spectroscopy and chemometrics was proposed. Raman spectroscopy was used to acquire spectral data of honey samples, and principal component analysis (PCA) was used to extract features from the spectral data. Principal components with a cumulative contribution rate of more than 85% were selected for modeling and prediction. By using linear discriminant analysis (LDA) and partial least squares-discriminant analysis (PLS-DA), models to identify honey adulterated with 20% syrup were established. A support vector machine (SVM) model to identify honey adulterated with 5% syrup, and all LDA, PLS-DA and SVM models could distinguish adulterated honey samples with 1% syrup content from pure honey with an accuracy of more than 0.9. Raman spectroscopy combined with chemometrics is a fast and non-destructive method for the identification of adulterated honey with high accuracy, which is significant to maintaining the order of the honey market.

Key words: honey; adulteration with syrup; Raman spectroscopy; principal component analysis; linear discriminant analysis; partial least squares-discriminant analysis; support vector machine