Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method
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METHODOLOGY ARTICLE
Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method Li Zhang 1
&
Zhongchen Wu 2 & Zongcheng Ling 2
Received: 5 June 2020 / Accepted: 3 August 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative analysis of chemical composition on planetary surface. Specifically, the quantitative compositional analysis method is a significant challenge for LIBS instrument onboard the Mars Science Laboratory (MSL) rover Curiosity ChemCam. Partial Least Squares (PLS) sub-model strategy is one of the outstanding multivariate analysis methods for calibration modeling, which is firstly developed by ChemCam science team. However, a troubling key issue is there are many parameters that need to be optimized, which increases the uncertainty of predicting outcomes and is time-consuming. In this study, an automatic parameters selection method based on Particle Swarm Optimization (PSO) tool is introduced. In the process of PSO iteration, RMSE minimization is taken as fitness, and finally the optimal sub-model parameters set is searched. In this way, the authors also get the best PLS latent variables of each sub-model by traversal method. Finally, the PSO PLS sub-model (PSO-PLS-SM) gets significant improvement in accuracy for the expanded Chemcam standards (408). And the RMSE of Si, Al, Ca, Na elements has been reduced by more than 20% relative to the conventional predictions. Keywords Laser-induced breakdown spectroscopy (LIBS) . ChemCam . Quantitative analysis . PLS sub-model . Particle Swarm Optimization (PSO)
Introduction Laser induced breakdown spectroscopy (LIBS) is one of the most valuable and rapid analysis techniques because it is able to rapidly obtain multi-elemental information with little or without sample preparation (Zhang et al. 2015; El Haddad et al. 2014) in a remote way. The largest LIBS publicavailable dataset may be provided by the ChemCam LIBS instrument on Curiosity rover, which has obtained more than 650,000 spectra of rock and soil since landing in Gale Crater in 2012 (Anderson et al. 2017).The ChemCam instrument suite on Curiosity, the Mars Science Laboratory (MSL) rover, uses the first LIBS instrument in the planetary mission to * Li Zhang [email protected] 1
School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
2
Shandong Provincial Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, Institute of Space Sciences, Shandong University, Weihai 264209, China
provide remote compositional information. With LIBS technology, it is possible to make the concurrent capability analysis covering all chemical elements except some noble gases. This technique has a significant advantage in abundance determinations of the light elements critical to understanding organic chemistry and to habitability issues but cannot be normally analyzed by X-ray techniques, such as H, Li, Be, B, C
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