Extending the spectral database of laser-induced breakdown spectroscopy with generative adversarial nets

Abstract

As a famous spectroscopy method for substance detection and classification, laser-induced breakdown spectroscopy (LIBS) is not a nondestructive detection method. Considering the precious samples and the experimental environment, sometimes it is difficult to get enough spectra to build the classification model, which is important for qualitative analysis. In this paper, a spectral generation method for extending the spectral database of LIBS is proposed based on generative adversarial nets (GAN). After enough interactive training, the generated spectra looked very similar to the experimental spectra. Evaluated with unsupervised clustering methods PCA and K-means, the generated spectra could not be distinguished from the real spectra. For each type of sample, most of the simulated spectra and experimental spectra were clustered into the same class, which meant the proposed method was effective to extend the spectral database. Using the spectral database extended by this method as training set data to build the SVM model, the results showed that when there were only a few experimental spectra, the combination of the generated spectra and the experimental spectra for building the classification model could achieve better identification results.