Discrimination of Salvia miltiorrhiza from Different Geographical Origins by Laser-Induced Breakdown Spectroscopy (LIBS) with Convolutional Neural Network (CNN)

AbstractAbstractA 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.Keywords: Convolutional neural network (CNN)geographical originlaser-induced breakdown spectroscopy (LIBS)Salvia miltiorrhiza Conflicts of interestThe authors declare that they have no known competing financial interests or personal relationships that influenced the work reported in this paper.Additional informationFundingThe authors appreciate the support of the National Natural Science Foundation of China (21807068, 21775118, 22177066), the Natural Science Foundation of Shaanxi Province (2020KJXX-030, 2021KJXX-51), Research Project of Shaanxi Universities Youth Innovation Team (21JP097), National College Student Innovation and Entrepreneurship Training Program Project (S202010705040), Xi'an Shiyou University Youth Research and Innovation Team Construction Plan (2019QNKYCXTD17), and Xi'an Shiyou University Graduate Innovation and Practice Ability Training Project (YCS21211036 and YCS21212113).