Classification of plastics with laser-induced breakdown spectroscopy based on principal component analysis and artificial neural network model

Qian Qian Wang*, Zhi Wen Huang, Kai Liu, Wen Jiang Li, Ji Xiang Yan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)

Abstract

The classification of seven kinds of plastic(ABS, PET, PP, PS, PVC, HDPE and PMMA) with the laser-induced breakdown spectroscopy based on artificial neural network model was investigated in the present paper. One hundred seventy LIBS spectra for each type of plastic were collected. Firstly, all 1 190 plastics LIBS spectra were studied with principal component analysis. The first five principal components (PC) totally explain 78.4% of the original spectrum information. Therefore, the scores of five PCs of 130 LIBS spectra for each kind of plastic were chosen as the training set to build a back-propagation artificial network model. And the other 40 LIBS spectra of each sample were used as the testing set for the trained model. The classification accuracy was 97.5%. Experimental results demonstrate that plastics can be classified by using principal component analysis and artificial neural network (BP) method.

Original languageEnglish
Pages (from-to)3179-3182
Number of pages4
JournalGuang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis
Volume32
Issue number12
DOIs
Publication statusPublished - Dec 2012

Keywords

  • Artificial neural network(BP)
  • Laser-induced breakdown spectroscopy (LIBS)
  • Material classification
  • Plastics
  • Principal component analysis(PCA)

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Wang, Q. Q., Huang, Z. W., Liu, K., Li, W. J., & Yan, J. X. (2012). Classification of plastics with laser-induced breakdown spectroscopy based on principal component analysis and artificial neural network model. Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis, 32(12), 3179-3182. https://doi.org/10.3964/j.issn.1000-0593(2012)12-3179-04