Fast Identification of Plastics with Laser-Induced Breakdown Spectroscopy

Qian Qian Sun, Min Du, Lian Bo Guo, Zhong Qi Hao, Rong Xing Yi, Jia Ming Li, Jian Guo Liu, Meng Shen, Xiang You Li, Xiao Yan Zeng, Yong Feng Lu

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Laser-induced breakdown spectroscopy (LIBS) combined with support vector machine (SVM) was adopted to identify 20 kinds of different colored industrial plastics from different manufacturers in open air. The experimental parameters of spectral acquisition were optimized firstly. 100 spectra recorded under optimum conditions were randomly and equally divided into training set and test set. 6 non-metallic characteristic spectral lines were used to avoid the interference with metallic lines. And the training time of SVM model was reduced. The results show that 996 of 1000 test spectra were identified correctly and the average classification accuracy is reached to 99.6%. The classification efficiency is improved with 6 non-metallic characteristic spectral lines. The research demonstrates that, when fewer of major non-metallic characteristic spectral lines are used, laser-induced breakdown spectroscopy technique with support vector machine can identify more kinds of plastics with high accuracy and efficiency.

Original languageEnglish (US)
Pages (from-to)2205-2209
Number of pages5
JournalGuang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis
Volume37
Issue number7
DOIs
StatePublished - Jul 1 2017

Keywords

  • Characteristic spectral lines of non-metallic elements
  • Laser-induced breakdown spectroscopy
  • Plastics identification
  • Support vector machine

ASJC Scopus subject areas

  • Instrumentation
  • Spectroscopy

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  • Cite this

    Sun, Q. Q., Du, M., Guo, L. B., Hao, Z. Q., Yi, R. X., Li, J. M., Liu, J. G., Shen, M., Li, X. Y., Zeng, X. Y., & Lu, Y. F. (2017). Fast Identification of Plastics with Laser-Induced Breakdown Spectroscopy. Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis, 37(7), 2205-2209. https://doi.org/10.3964/j.issn.1000-0593(2017)07-2205-05