The objective of the present research is to study the potential of using Fourier transform near-infrared spectroscopy (FT-NIR) in conjunction with discriminant partial least squares (DPLS) chemometric techniques for the discrimination of honey authenticity. First, seventy one commercial honey samples from Chinese market were analyzed to detect the levels of honey adulteration by stable carbon isotope ratio and the chemical result showed that the samples include unadulterated (n = 27) and adulterated (n = 44) products. The samples were scanned in the spectral region between 4 000 and 11 000 cm(-1) by FT-NIR spectrometer with an optic fiber of 2 mm path-length and an InGaAs detector and then divided randomly five times into two sets, namely calibration sets and validation sets, respectively. Five kinds of mathematic models of honey samples were established for classification of honeys as authentic or adulterated by using DPLS. Different spectra pretreatment methods, spectral range and different principal component factors were selected to optimize the calibration models. The calibration models were successfully validated with exterior cross-validation methods. Through comparison analysis of the results, the overall corrected identification rate of authentic and adulterated honey samples in five calibration models were 91.49%, 94.68%, 92.98%, 93.86% and 94.87%, respectively. The correct classification rate of the validation samples was 93.75%, 89.58%, 89.29%, 92.31% and 86.96% from model one to model five, respectively and 100% of adulterated honey samples were correctly identified and classified in validation models 2, 3 and 4. The results demonstrated that FT-NIR together with DPLS could be used as a rapid and cost-efficient screening tool for discrimination of commercial honey adulteration, and the analytical technique would be significant to Chinese honey quality supervision.