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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.

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