Abstract
Few studies have been carried out on the discrimination of precious Tetrastigma hemsleyanum, also known as Sanyeqing in China. Fourier transform near-infrared (FT-NIR) spectroscopy coupled with chemometric class-modelling techniques to rapidly and effectively discriminate T. hemsleyanum was investigated in this study. A relevance vector machine (RVM) was used to build a stable and accurate model. Furthermore, competitive adaptive reweighted sampling (CARS) was employed to extract effective wavelength variables. The results indicated that the accuracy of the RVM model was satisfactory due to a good discrimination rate. Additionally, the variable number of the CARS model was validly improved with a wavelength variable of 26, and the results of the RVM-CARS model were also effective. The results suggested that the CARS-RVM model is a suitable model to rapidly and efficiently discriminate T. hemsleyanum.