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

Liang Han,Li Zhang,Feng Liu

doi:10.3390/sym13020319

Liang Han, Li Zhang + Show 1 more

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https://doi.org/10.3390/sym13020319

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Journal: Symmetry	Publication Date: Feb 15, 2021
Citations: 5	License type: CC BY 4.0

Affiliation: Beijing Jiaotong University, Shandong University

Abstract

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

Highlights

As a powerful and convenient technique, laser-induced breakdown spectroscopy (LIBS) is utilized to produce the spectrum with some multivariate regression algorithms in order to obtain the relative content of each compound in samples
In order to reduce the errors caused by the misclassification of the support vector machine (SVM) classifiers, we presented an optimized method which corrected the output of a sub-model by using the output of the corresponding full model
partial least squares regression (PLSR) sub-models had lower root mean square error of prediction (RMSEP), which indicated that the performances of sub-models were well

Summary

Introduction

As a powerful and convenient technique, laser-induced breakdown spectroscopy (LIBS) is utilized to produce the spectrum with some multivariate regression algorithms in order to obtain the relative content of each compound in samples. Calibration-free (CF)-LIBS is another analysis approach, which compensates for matrix effects through a model without the need for calibration curves produced by standards [9]. One of the wellknown methods was proposed by Clegg et al They developed a “sub-model” method for improving the accuracy of quantitative target composition determinations by adopting LIBS. By using several regression models, which trained on a restricted composition range, and these models “blended” using a simple linear weighted sum. Accurate predictions could be obtained over a wide range of target compositions [12]. The artificial neural network (ANN) was employed to establish the qualitative or quantitative model, in which the collected spectral value at every wavelength was fed to the input layer [13]

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