Authenticity Identification of Panax notoginseng by Terahertz Spectroscopy Combined with LS-SVM

Due to the time consuming and great complexity of traditional identification methods, the authenticity of Panax notoginseng is identified by terahertz time domain spectroscopy (THz-TDS) combined with a least square support vector machine (LS-SVM) in our study. The original time domain and frequency domain spectral information of Panax notoginseng are firstly collected in a frequency range of 0.2–2.0 THz. The spectral data are pretreated with the standard normal variate (SNV) algorithm. Then, the competitive adaptive reweighted sampling (CARS) algorithm is used to screen the frequency domain spectral data to obtain the final characteristic spectral data. Finally, the identification models of support vector machine (SVM), LS-SVM and backpropagation neural network (BPNN) are established with the screened spectral data. By comparison, it is found that the LS-SVM model has a better identification result with an accuracy of 90.4% and an error rate of 9.6%, respectively. This suggests that the LS-SVM model has a higher identification accuracy. Research results show that THz-TDS can effectively obtain the information of Panax notoginseng. The THz-TDS combined with LS-SVM can be used to effectively identify the authenticity of Panax notoginseng, which has a practical significance and broad application potential.