Chemometric methods for botanical classification of Chinese honey based on the volatile compound profile

This study develops a method for the discrimination and prediction of honey samples from various botanical origins. The method was based on the nontargeted volatile profiles obtained by solid-phase microextraction and gas chromatography-mass spectrometry (SPME-GC/MS) combined with chemometrics. The blind analysis of nontargeted volatile profiles was carried out by SPME-GC/MS for 87 authentic honey samples from four botanical origins including acacia, linden, vitex, and rape honey. Quality control of the samples was performed by Principal Component Analysis (PCA). Then, sample class prediction models based on partial least squares discriminant analysis (PLS-DA), naïve Bayes (NB), and back-propagation artificial neural network (BP-ANN) were constructed. The 100 % accuracy results revealed a perfect classification among the different botanical origins. The results indicated that all could be predicted correctly. In addition, the reliability and practicability of the models were validated by an independent set of an additional 20 authentic honey samples. All 20 samples were accurately classified. Finally, the characteristic volatile compounds of linden honey were tentatively identified. It is suggested that the proposed method is reliable and accurate for the classification of honey from various botanical origins, as well as finding marker compounds. Chemometric methods for botanical classification of Chinese honey based on the volatile compound profile