scholarly journals Quantitative Determination of Cd in Soil Using Laser-Induced Breakdown Spectroscopy in Air and Ar Conditions

Molecules ◽  
2018 ◽  
Vol 23 (10) ◽  
pp. 2492 ◽  
Author(s):  
Xiaodan Liu ◽  
Fei Liu ◽  
Weihao Huang ◽  
Jiyu Peng ◽  
Tingting Shen ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Least Squares ◽  
Three Dimensional ◽  
Accurate Method ◽  
Laser Induced Breakdown Spectroscopy ◽  
Quantitative Detection ◽  
Support Vector ◽  
Least Squares Regression ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Rapid detection of Cd content in soil is beneficial to the prevention of soil heavy metal pollution. In this study, we aimed at exploring the rapid quantitative detection ability of laser- induced breakdown spectroscopy (LIBS) under the conditions of air and Ar for Cd in soil, and finding a fast and accurate method for quantitative detection of heavy metal elements in soil. Spectral intensity of Cd and system performance under air and Ar conditions were analyzed and compared. The univariate model and multivariate models of partial least-squares regression (PLSR) and least-squares support vector machine (LS-SVM) of Cd under the air and Ar conditions were built, and the LS-SVM model under the Ar condition obtained the best performance. In addition, the principle of influence of Ar on LIBS detection was investigated by analyzing the three-dimensional profile of the ablation crater. The overall results indicated that LIBS combined with LS-SVM under the Ar condition could be a useful tool for the accurate quantitative detection of Cd in soil and could provide reference for environmental monitoring.

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 High-Sensitivity Determination of Nutrient Elements in Panax notoginseng by Laser-induced Breakdown Spectroscopy and Chemometric Methods

Molecules ◽  
2019 ◽  
Vol 24 (8) ◽  
pp. 1525 ◽  
Author(s):  
Tingting Shen ◽  
Weijiao Li ◽  
Xi Zhang ◽  
Wenwen Kong ◽  
Fei Liu ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Least Squares ◽  
Panax Notoginseng ◽  
High Accuracy ◽  
Laser Induced Breakdown Spectroscopy ◽  
Quantitative Detection ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

High-accuracy and fast detection of nutritive elements in traditional Chinese medicine Panax notoginseng (PN) is beneficial for providing useful assessment of the healthy alimentation and pharmaceutical value of PN herbs. Laser-induced breakdown spectroscopy (LIBS) was applied for high-accuracy and fast quantitative detection of six nutritive elements in PN samples from eight producing areas. More than 20,000 LIBS spectral variables were obtained to show elemental differences in PN samples. Univariate and multivariate calibrations were used to analyze the quantitative relationship between spectral variables and elements. Multivariate calibration based on full spectra and selected variables by the least absolute shrinkage and selection operator (Lasso) weights was used to compare the prediction ability of the partial least-squares regression (PLS), least-squares support vector machines (LS-SVM), and Lasso models. More than 90 emission lines for elements in PN were found and located. Univariate analysis was negatively interfered by matrix effects. For potassium, calcium, magnesium, zinc, and boron, LS-SVM models based on the selected variables obtained the best prediction performance with Rp values of 0.9546, 0.9176, 0.9412, 0.9665, and 0.9569 and root mean squared error of prediction (RMSEP) of 0.7704 mg/g, 0.0712 mg/g, 0.1000 mg/g, 0.0012 mg/g, and 0.0008 mg/g, respectively. For iron, the Lasso model based on full spectra obtained the best result with an Rp value of 0.9348 and RMSEP of 0.0726 mg/g. The results indicated that the LIBS technique coupled with proper multivariate chemometrics could be an accurate and fast method in the determination of PN nutritive elements for traditional Chinese medicine management and pharmaceutical analysis.

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.

Analytical spectral dependent partial least squares regression: a study of nuclear waste glass from thorium based fuel using LIBS

2015 ◽  
Vol 30 (12) ◽  
pp. 2507-2515 ◽  
Author(s):  
Manjeet Singh ◽  
Vijay Karki ◽  
Raman K. Mishra ◽  
Amar Kumar ◽  
C. P. Kaushik ◽  
...  
Keyword(s):  
Least Squares ◽  
Nuclear Waste ◽  
Partial Least Squares Regression ◽  
Waste Glass ◽  
Laser Induced Breakdown Spectroscopy ◽  
Least Squares Regression ◽  
Breakdown Spectroscopy ◽  
Spectral Modification ◽  
Nuclear Waste Glass ◽  
Laser Induced Breakdown

LIBS (Laser Induced Breakdown Spectroscopy) for simultaneous multielement quantification of nuclear waste glass using a spectral modification based PLSR approach.

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