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Classification accuracy improvement by data preprocessing in handheld laser-induced breakdown spectroscopy

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Abstract

Qualitative analysis using handheld laser-induced breakdown spectroscopy (HH-LIBS) usually suffers from spectral fluctuation. To reduce spectral discreteness and improve classification accuracy, 3 different data preprocessing methods namely minimum standard deviation (MSD), minimum distance (MD), and Weibull distribution (WD) were proposed. The classifications of 15 rock samples using the linear discriminant analysis (LDA) algorithm assisted with these preprocessing methods were carried out. The results showed that the relative standard deviations (RSDs) of the spectral intensities were reduced from 44.39% of the original spectra to 25.28, 19.67, and 27.26%, respectively. The classification accuracies of the rock samples were increased from 93.07% to 99.05, 97.04, and 99%, respectively. The results demonstrate that the preprocessing methods provide an effective approach for improving the analytical performance of HH-LIBS.

Graphical abstract: Classification accuracy improvement by data preprocessing in handheld laser-induced breakdown spectroscopy

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Publication details

The article was received on 17 Jul 2019, accepted on 09 Sep 2019 and first published on 13 Sep 2019


Article type: Paper
DOI: 10.1039/C9AY01524H
Anal. Methods, 2019, Advance Article

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    Classification accuracy improvement by data preprocessing in handheld laser-induced breakdown spectroscopy

    J. Yan, P. Yang, R. Zhou, S. Li, K. Liu, W. Zhang, X. Li, D. Wang, X. Zeng and Y. Lu, Anal. Methods, 2019, Advance Article , DOI: 10.1039/C9AY01524H

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