Classification of honey applying high performance liquid chromatography, near-infrared spectroscopy and chemometrics

The potential of Fourier Transform Near-Infrared spectroscopy (FT-NIR) and High-Performance Liquid Chromatography with Diode-Array Detection (HPLC-DAD) in combination with multivariate data analysis was examined to classify 70 honey samples (belonging to 7 different varieties) according to their botanical origin. In the first part of the work, classification was achieved by applying PLS-DA to the individual data blocks: this approach led to promising results from the prediction point of view. In the second part of the study, the multi-block data set has been handled by data-fusion techniques which led to comparable or better results than those obtained by the analysis of individual matrices. These satisfactory results confirm the feasibility of the proposed methodology and encourage the development of similar approaches for honey quality assessment.