An Improved Sub-Model PLSR Quantitative Analysis Method Based on SVM Classifier for ChemCam Laser-Induced Breakdown Spectroscopy

نویسندگان

چکیده

Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative and quantitative analysis. Component analysis significant issue the LIBS instrument onboard Mars Science Laboratory (MSL) rover Curiosity ChemCam SuperCam on 2020 rover. The partial least squares (PLS) sub-model strategy one of outstanding multivariate methods calibration modeling, which firstly developed by science team. We innovatively used support vector machine (SVM) classifier to select corresponding sub-model. Then conventional regression approaches (PLSR) was utilized as prove that our selecting method feasible, effective, well-performed. For eight oxides, i.e., SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, modified SVM-PLSR blended 34.8% 62.4% lower than root mean square error prediction (RMSEP) full model method. In order avoid SVM classifiers classifying spectrum into an incorrect class, optimized proposed worked well in PLSR sub-models.

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ژورنال

عنوان ژورنال: Symmetry

سال: 2021

ISSN: ['0865-4824', '2226-1877']

DOI: https://doi.org/10.3390/sym13020319