Résumé
OBJECTIVE: To discriminate and quantify of Gleditsia japonica Miq. thorn (SZJ) and Gleditsia microphylla Gordon ex Y. T. Lee thorn (YZJ) in the Gleditsia sinensis Lam thorn (GST). METHODS: Fourier transform near-infrared spectroscopy (FT-NIR) combined with linear discriminate analysis (LDA), support vector machine (SVM), as while as back propagation neural network (BPNN) algorithms were applied to construct the identification models. The SZJ and YZJ content in adulterated GST were determined by partial least squares regression (PLSR). RESULTS: The SVM models performance best compared with LDA and BP-NN models for it could reach 100% accuracy in training and validation set for identifying authentic GST and GST adulterated with SZJ and YZJ based on the spectral region of 5 000-4 200 cm-1 combined with SG+VN processing. The rp, RMSEP (the root mean standard error of prediction) and bias for the prediction by PLS regression model were 0.993, 2.919% and -0.330 3 for SZJ, 0.995, 2.57% and 0.364 9 for YZJ, respectively. CONCLUSION: Our results suggest that the combination of NIR spectroscopy and chemometric methods offers a simple, fast and reliable method for classifification and quantifification of SZJ and YZJ adulterants in the GST.