Quantitative analysis modeling for the ChemCam spectral data based on laser-induced breakdown spectroscopy using convolutional neural network

Xueqiang CAO (曹学强); Li ZHANG (张立); Zhongchen WU (武中臣); Zongcheng LING (凌宗成); Jialun LI (李加伦); Kaichen GUO (郭恺琛)

doi:10.1088/2058-6272/aba5f6

Plasma Science and Technology > 2020 > 22(11): 115502. > DOI: 10.1088/2058-6272/aba5f6

Xueqiang CAO (曹学强), Li ZHANG (张立), Zhongchen WU (武中臣), Zongcheng LING (凌宗成), Jialun LI (李加伦), Kaichen GUO (郭恺琛). Quantitative analysis modeling for the ChemCam spectral data based on laser-induced breakdown spectroscopy using convolutional neural network[J]. Plasma Science and Technology, 2020, 22(11): 115502. DOI: 10.1088/2058-6272/aba5f6

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Quantitative analysis modeling for the ChemCam spectral data based on laser-induced breakdown spectroscopy using convolutional neural network

¹ School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, People's Republic of China
² Shandong Provincial Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, Institute of Space Sciences, Shandong University, Weihai 264209, People's Republic of China

Funds: This work is supported by the Pre-research project on Civil Aerospace Technologies (No. D020102) funded by China National Space Administration (CNSA). We also thank the funding from National Natural Science Foundation of China (Nos. U1931211 and 41573056), the Natural Science Foundation of Shandong Province (No. ZR2019MD008) and the Major Research Project of Shandong Province (No. GG201809130208).

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Received Date: March 26, 2020
Revised Date: July 12, 2020
Accepted Date: July 13, 2020

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Abstract

Abstract

Laser-induced breakdown spectroscopy (LIBS) has been applied to many fields for the quantitative analysis of diverse materials. Improving the prediction accuracy of LIBS regression models is still of great significance for the Mars exploration in the near future. In this study, we explored the quantitative analysis of LIBS for the one-dimensional ChemCam (an instrument containing a LIBS spectrometer and a Remote Micro-Imager) spectral data whose spectra are produced by the ChemCam team using LIBS under the Mars-like atmospheric conditions. We constructed a convolutional neural network (CNN) regression model with unified parameters for all oxides, which is efficient and concise. CNN that has the excellent capability of feature extraction can effectively overcome the chemical matrix effects that impede the prediction accuracy of regression models. Firstly, we explored the effects of four activation functions on the performance of the CNN model. The results show that the CNN model with the hyperbolic tangent (tanh) function outperforms the CNN models with the other activation functions (the rectified linear unit function, the linear function and the Sigmoid function). Secondly, we compared the performance among the CNN models using different optimization methods. The CNN model with the stochastic gradient descent optimization and the initial learning rate=0.0005 achieves satisfactory performance compared to the other CNN models. Finally, we compared the performance of the CNN model, the model based on support vector regression (SVR) and the model based on partial least square regression (PLSR). The results exhibit the CNN model is superior to the SVR model and the PLSR model for all oxides. Based on the above analysis, we conclude the CNN regression model can effectively improve the prediction accuracy of LIBS.
- laser-induced breakdown spectroscopy,
- convolutional neural network,
- activationfunction,
- optimization method,
- quantitative analysis

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Quantitative analysis modeling for the ChemCam spectral data based on laser-induced breakdown spectroscopy using convolutional neural network