Determination of Rhizoma curcumaes Using Visible and Near-Infrared Spectroscopy

Abstract

This study investigated the capacity of visible and near infrared (VIS-NIR) spectroscopy with chemometrics for the fast species discrimination of Rhizoma curcumaes. The powder samples of three Curcuma species i.e., C. phaeocaulis, C. kwangsiensis and C. wenyujin, from five regions in China were used for the analysis. Least squares support vector machine (LS-SVM) was used to establish the discrimination model. Multiplicative scatter correction (MSC) was chosen as the best spectral pre-processing algorithm. Successive projections algorithm (SPA) was operated for the wavelength variable selection from thousands of original full-spectrum (FS) variables. The best correct classification rate of 99.11 % was obtained by MSC-SPA-LS-SVM model with only eight variables. The overall results demonstrate that VIS-NIR spectroscopy has the ability for the fast discrimination of different species of Curcuma species.