A data selection method for matrix effects and uncertainty reduction for laser-induced breakdown spectroscopy

Jie LONG; Weiran SONG; Zongyu HOU; Zhe WANG

doi:10.1088/2058-6272/acb6dd

Plasma Science and Technology > 2023 > 25(7): 075501. > DOI: 10.1088/2058-6272/acb6dd

Jie LONG, Weiran SONG, Zongyu HOU, Zhe WANG. A data selection method for matrix effects and uncertainty reduction for laser-induced breakdown spectroscopy[J]. Plasma Science and Technology, 2023, 25(7): 075501. DOI: 10.1088/2058-6272/acb6dd

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A data selection method for matrix effects and uncertainty reduction for laser-induced breakdown spectroscopy

1.
State Key Laboratory of Power System Operation and Control Systems, International Joint Laboratory on Low Carbon Clean Energy Innovation, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, People's Republic of China
2.
Shanxi Research Institute for Clean Energy, Tsinghua University, Taiyuan 030032, People's Republic of China

More Information

Corresponding author:
Zhe WANG, E-mail: Zongyuhou@tsinghua.edu.cn

Zongyu HOU, E-mail: zhewang@tsinghua.edu.cn
Received Date: October 24, 2022
Revised Date: January 24, 2023
Accepted Date: January 27, 2023
Available Online: December 05, 2023
Published Date: April 12, 2023

Graphical Abstract

Abstract

Abstract

Severe matrix effects and high signal uncertainty are two key bottlenecks for the quantitative performance and wide applications of laser-induced breakdown spectroscopy (LIBS). Based on the understanding that the superposition of both matrix effects and signal uncertainty directly affects plasma parameters and further influences spectral intensity and LIBS quantification performance, a data selection method based on plasma temperature matching (DSPTM) was proposed to reduce both matrix effects and signal uncertainty. By selecting spectra with smaller plasma temperature differences for all samples, the proposed method was able to build up the quantification model to rely more on spectra with smaller matrix effects and signal uncertainty, therefore improving final quantification performance. When applied to quantitative analysis of the zinc content in brass alloys, it was found that both accuracy and precision were improved using either a univariate model or multiple linear regression (MLR). More specifically, for the univariate model, the root-mean-square error of prediction (RMSEP), the determination coefficients (R²) and relative standard derivation (RSD) were improved from 3.30%, 0.864 and 18.8% to 1.06%, 0.986 and 13.5%, respectively; while for MLR, RMSEP, R² and RSD were improved from 3.22%, 0.871 and 26.2% to 1.07%, 0.986 and 17.4%, respectively. These results prove that DSPTM can be used as an effective method to reduce matrix effects and improve repeatability by selecting reliable data.
- laser-induced breakdown spectroscopy (LIBS),
- quantification,
- uncertainty,
- univariate/multivariate analysis,
- matrix effects,
- temperature matching

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Acknowledgments

The authors are grateful for the financial support from the Scientific Research Program for Young Talents of China National Nuclear Corporation (2020), National Natural Science Foundation of China (Nos. 51906124 and 62205172), Shanxi Province Science and Technology Department (No. 20201101013) and Guoneng Bengbu Power Generation Co., Ltd (No. 20212000001).

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