A Nonlinearized Multivariate Dominant Factor–Based Partial Least Squares (PLS) Model for Coal Analysis by Using Laser-Induced Breakdown Spectroscopy

2013 ◽  
Vol 67 (3) ◽  
pp. 291-300 ◽  
Author(s):  
Jie Feng ◽  
Zhe Wang ◽  
Lizhi Li ◽  
Zheng Li ◽  
Weidou Ni
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Dominant Factor ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Coal Analysis ◽  
Laser Induced Breakdown

scholarly journals Correction: Accuracy improvement of boron by molecular emission with a genetic algorithm and partial least squares regression model in laser-induced breakdown spectroscopy

2020 ◽  
Vol 35 (7) ◽  
pp. 1498-1498
Author(s):  
Zhihao Zhu ◽  
Jiaming Li ◽  
Yangmin Guo ◽  
Xiao Cheng ◽  
Yun Tang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Regression Model ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Laser Induced Breakdown Spectroscopy ◽  
Least Squares Regression ◽  
Accuracy Improvement ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Correction for ‘Accuracy improvement of boron by molecular emission with a genetic algorithm and partial least squares regression model in laser-induced breakdown spectroscopy’ by Zhihao Zhu et al., J. Anal. At. Spectrom., 2018, 33, 205–209, DOI: 10.1039/C7JA00356K.

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

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 319
Author(s):  
Liang Han ◽  
Feng Liu ◽  
Li Zhang
Keyword(s):  
Quantitative Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Svm Classifier ◽  
Least Squares Regression ◽  
Breakdown Spectroscopy ◽  
Model Method ◽  
Laser Induced Breakdown

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.

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