Discrimination of <i>Salvia miltiorrhiza</i> from Different Geographical Origins by Laser-Induced Breakdown Spectroscopy (LIBS) with Convolutional Neural Network (CNN)
Long Jiao, Chengyu Sun, Naying Yan, Chun‐Hua Yan, Le Qu, Qin Wang, Shengrui Zhang, Ling Ma
Abstract
A method for discriminating Salvia miltiorrhiza from different geographical origins was developed using laser-induced breakdown spectroscopy (LIBS) with convolutional neural network (CNN). The LIBS spectra of Salvia miltiorrhiza samples from six geographical locations were preprocessed with the maximum minimum normalization method. The classification model for discriminating these samples was developed by using a one-dimensional convolutional neural network. The discrimination accuracy of the developed CNN model reached 97.09%. Compared with support vector machine and k-nearest neighbor methods, the CNN model showed higher discrimination accuracy. The results demonstrate that the combination of LIBS and CNN is suitable for discriminating Salvia miltiorrhiza from different geographical locations.