scholarly journals Quantitative analysis of cadmium in navel orange by laser-induced breakdown spectroscopy combined with partial least squares

2013 ◽  
Vol 62 (4) ◽  
pp. 044211
Author(s):  
Zhang Xu ◽  
Yao Ming-Yin ◽  
Liu Mu-Hua
Keyword(s):  
Quantitative Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Laser Induced Breakdown Spectroscopy ◽  
Navel Orange ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Classical univariate calibration and partial least squares for quantitative analysis of brass samples by laser-induced breakdown spectroscopy

2010 ◽  
Vol 65 (8) ◽  
pp. 658-663 ◽  
Author(s):  
Jose Manuel Andrade ◽  
Gabriele Cristoforetti ◽  
Stefano Legnaioli ◽  
Giulia Lorenzetti ◽  
Vincenzo Palleschi ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

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.

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.

Sign in / Sign up

Export Citation Format

Share Document