Discrimination of Radix Isatidis and Rhizoma et Radix Baphicacanthis Cusia samples by near infrared spectroscopy with the aid of chemometrics

Abstract

A novel method for the discrimination of the three kinds of Indigowoad Root sample, Radix Isatidis (RI), Rhizoma et Radix Baphicacanthis Cusia (RRBC) and simulated adulterated samples (AD) was researched and developed with the use of near infrared spectroscopy (NIR) and chemometrics. Principal component analysis (PCA) was applied to process the NIR data of 75 collected Indigowoad Root samples, and the three kinds of such sample were discriminated along the first principal component (PC1) axis. In addition, the data pretreatment methods – genetic algorithm-partial least squares (GA-PLS), successive projections algorithm (SPA), and wavelet transform (WT), were employed to select the key analytical wavelengths, and then, these were used as input variables for the three kinds of the pattern recognition method, such as K-nearest neighbor (KNN), radial basis function-artificial neural network (RBF-ANN), least squares-support vector machine (LS-SVM) and back propagation-artificial neural network (BP-ANN). The WT was the method of choice for data pretreatment, and three pretreatment-prediction method combinations performed well (basis: %recognition rate) – WT-KNN (98.2%) and BP-ANN (97.3%) as well as GA-PLS – LS-SVM (97.2). A BP-ANN calibration model was built for the quantitative discrimination of the three types of the complex Indigowoad Root samples, and it was successfully validated.