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

Jiujiang Yan, Ping Yang, Ran Zhou, Shuhan Li, Kun Liu, Wen Zhang, Xiangyou Li, Dengzhi Wang, Xiaoyan Zeng, Yongfeng Lu

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)5177-5184
Number of pages8
JournalAnalytical Methods
Volume11
Issue number40
DOIs
Publication statusPublished - Oct 28 2019

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ASJC Scopus subject areas

  • Analytical Chemistry
  • Chemical Engineering(all)
  • Engineering(all)

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