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Four-metal-element quantitative analysis and pollution source discrimination in atmospheric sedimentation by laser-induced breakdown spectroscopy (LIBS) coupled with machine learning

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Abstract

The laser-induced breakdown spectroscopy (LIBS) technique coupled with random forest (RF) and least squares support vector machine (LSSVM) methods was proposed to perform the quantitative and classification analyses of atmospheric sedimentation. The LIBS spectra of 16 atmospheric sedimentation samples with different locations were obtained via the LIBS measurement system, and the major elements of the atmospheric sedimentation samples were identified by the National Institute of Standards and Technology (NIST) database. For quantitative analysis, first, the best pretreatment method needs to be selected to process the LIBS spectra of the four metal elements (Pb, Cu, Zn and Al) of atmospheric sedimentation samples obtained from 16 locations. Then, RF, LSSVM and PLS calibration models were constructed with the optimal pretreatment spectra as input variables. The performances of the three calibration models were compared by the correlation coefficient of cross-validation (RCV2) and root mean square error of cross-validation (RMSECV) to obtain an optimal model. Finally, the optimal model was verified by the correlation coefficient of prediction (RP2) and root mean square error of prediction (RMSEP). The satisfactory quantitative results of Pb, Cu and Al are the RF calibration model, and Zn is the LSSVM calibration model. For classification analysis, first, the best pretreatment method needs to be selected to process the LIBS spectra of the atmospheric sedimentation samples. Then, RF, LSSVM and PLS-DA were constructed with the best pretreatment spectra as input variables. Finally, the five factors, i.e., accuracy, sensitivity, precision, specificity and area under curve (AUC) were used to evaluate the predictive performance of the three classification models, and the LSSVM classification model exhibited better prediction in pollution source discrimination. It was confirmed that the LIBS technique coupled with the RF and LSSVM methods is a promising approach to achieve the analysis of atmospheric sedimentation.

Graphical abstract: Four-metal-element quantitative analysis and pollution source discrimination in atmospheric sedimentation by laser-induced breakdown spectroscopy (LIBS) coupled with machine learning

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Article information


Submitted
24 Oct 2019
Accepted
17 Dec 2019
First published
17 Dec 2019

J. Anal. At. Spectrom., 2020, Advance Article
Article type
Paper

Four-metal-element quantitative analysis and pollution source discrimination in atmospheric sedimentation by laser-induced breakdown spectroscopy (LIBS) coupled with machine learning

X. Zhang, N. Li, C. Yan, J. Zeng, T. Zhang and H. Li, J. Anal. At. Spectrom., 2020, Advance Article , DOI: 10.1039/C9JA00360F

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