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

2018 ◽  
Vol 33 (2) ◽  
pp. 205-209 ◽  
Author(s):  
Zhihao Zhu ◽  
Jiaming Li ◽  
Yangmin Guo ◽  
Xiao Cheng ◽  
Yun Tang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Partial Least Squares Regression ◽  
The Self ◽  
Laser Induced Breakdown Spectroscopy ◽  
Least Squares Regression ◽  
Absorption Effect ◽  
Accuracy Improvement ◽  
Molecular Emission ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

We chose BO molecular emission to reduce the self-absorption effect in atomic LIBS and applied GA-PLSR to improve the molecular calibration.

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.

scholarly journals Correction: Investigation of the self-absorption effect using spatially resolved laser-induced breakdown spectroscopy

2020 ◽  
Vol 35 (7) ◽  
pp. 1495-1495
Author(s):  
Rongxing Yi ◽  
Lianbo Guo ◽  
Changmao Li ◽  
Xinyan Yang ◽  
Jiaming Li ◽  
...  
Keyword(s):  
The Self ◽  
Laser Induced Breakdown Spectroscopy ◽  
Absorption Effect ◽  
Breakdown Spectroscopy ◽  
Spatially Resolved ◽  
Laser Induced Breakdown

Correction for ‘Investigation of the self-absorption effect using spatially resolved laser-induced breakdown spectroscopy’ by Rongxing Yi et al., J. Anal. At. Spectrom., 2016, 31, 961–967, DOI: 10.1039/C5JA00500K.

Investigation of the self-absorption effect using spatially resolved laser-induced breakdown spectroscopy

2016 ◽  
Vol 31 (4) ◽  
pp. 961-967 ◽  
Author(s):  
Rongxing Yi ◽  
Lianbo Guo ◽  
Changmao Li ◽  
Xinyan Yang ◽  
Jiaming Li ◽  
...  
Keyword(s):  
The Self ◽  
Laser Induced Breakdown Spectroscopy ◽  
Absorption Effect ◽  
Breakdown Spectroscopy ◽  
Spatially Resolved ◽  
Laser Induced Breakdown

This study discovered the distributional difference of self-absorption effect in laser-induced breakdown spectroscopy, and investigated the method to reduce the self-absorption effect.

scholarly journals Determination and Quantification of Heavy Metals in Sediments through Laser-Induced Breakdown Spectroscopy and Partial Least Squares Regression

2021 ◽  
Vol 11 (15) ◽  
pp. 7154
Author(s):  
Sangmi Yoon ◽  
Jaeseung Choi ◽  
Seung-Jae Moon ◽  
Jung Hyun Choi
Keyword(s):  
Heavy Metals ◽  
Root Mean Square Error ◽  
Partial Least Squares Regression ◽  
Laser Induced Breakdown Spectroscopy ◽  
Mean Square ◽  
Least Squares Regression ◽  
Sediment Samples ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Plsr Model

Conventional analysis techniques and sample preprocessing methods for identifying trace metals in soil and sediment samples are costly and time-consuming. This study investigated the determination and quantification of heavy metals in sediments by using a Laser-Induced Breakdown Spectroscopy (LIBS) system and multivariate chemometric analysis. Principle Component Analysis (PCA) was conducted on the LIBS spectra at the emission lines of 11 selected elements (Al, Ca, Cd, Cr, Fe, K, Mg, Na, Ni, Pb, and Si). The results showed apparent clustering of four types of sediment samples, suggesting the possibility of application of the LIBS technique for distinguishing different types of sediments. Mainly, the Cd, Cr, and Pb concentrations in the sediments were analyzed. A data-smoothing method—namely, the Savitzky–Golay (SG) derivative—was used to enhance the performance of the Partial Least Squares Regression (PLSR) model. The performance of the PLSR model was evaluated in terms of the coefficient of determination (R2), Root Mean Square Error of Calibration (RMSEC), and Root Mean Square Error of Cross Validation (RMSECV). The results obtained using the PLSR with the SG derivative were improved in terms of the R2 and RMSECV, except for Cr. In particular, the results for Cd obtained with the SG derivative showed a decrease of 25% in the RMSECV value. This demonstrated that the PLSR model with the SG derivative is suitable for the quantitative analysis of metal components in sediment samples and can play a significant role in controlling and managing the water quality of rivers.

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