A laser-induced breakdown spectroscopy multi-component quantitative analytical method based on a deep convolutional neural network

Abstract

One major technical difficulty of laser-induced breakdown spectroscopy (LIBS) lies in achieving ideal accuracy of quantitative determination of the multiple chemical components in a target sample. In this study, we propose a LIBS multi-component quantitative analytical method based on the construction of a deep convolutional neural network (CNN). More than 1400 LIBS spectra, collected from 23 China national standard reference materials, were utilized to train the CNN and validate its predictive ability as well. The experiment was implemented by the LIBS system in MarSCoDe, which would be the Mars Surface Composition Detector on the rover of China's first Mars exploration mission in 2020. To evaluate the performance of the CNN, we inspect the root mean square error (RMSE) value of the prediction, with both overall RMSE and component-wise RMSE considered, and we further look into the prediction relative error of each component. We compare the performance of the CNN with that of two alternative schemes based on back-propagation neural network (BPNN) and partial least squares (PLS) regression respectively, with the PLS scheme actually containing two methods, i. e. PLS1 and PLS2. Besides the examination of the specific values of RMSE and relative error, we have also carried out some statistical analysis to endow the comparison with statistical significance. Moreover, we investigate the effect of baseline removal preprocessing upon the predictive ability of each method. The results show that the CNN method has the best performance among the four methods in terms of overall accuracy, no matter the test is based on the spectra with or without baseline removal, and the superiority of the CNN over the other three methods is more significant in the latter case. Since the number of samples is relatively small, the results demonstrated in this work are preliminary and unsuitable for immediate generalization, but they indicate that the CNN-based methodology is a promising tool for LIBS quantitative analysis with good accuracy and high efficiency.