Cluster analysis of polymers using laser-induced breakdown spectroscopy with K-means

Yangmin Guo, Yun Tang, Yu Du, Shisong Tang, Lianbo Guo, Xiangyou Li, Yongfeng Lu, Xiaoyan Zeng

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Laser-induced breakdown spectroscopy (LIBS) combined with K-means algorithm was employed to automatically differentiate industrial polymers under atmospheric conditions. The unsupervised learning algorithm K-means were utilized for the clustering of LIBS dataset measured from twenty kinds of industrial polymers. To prevent the interference from metallic elements, three atomic emission lines (C i 247.86 nm , H i 656.3 nm, and O i 777.3 nm) and one molecular line C-N (0, 0) 388.3 nm were used. The cluster analysis results were obtained through an iterative process. The Davies-Bouldin index was employed to determine the initial number of clusters. The average relative standard deviation values of characteristic spectral lines were used as the iterative criterion. With the proposed approach, the classification accuracy for twenty kinds of industrial polymers achieved 99.6%. The results demonstrated that this approach has great potential for industrial polymers recycling by LIBS.

Original languageEnglish (US)
Article number065505
JournalPlasma Science and Technology
Issue number6
StatePublished - Jun 2018
Externally publishedYes


  • K-means
  • laser-induced breakdown spectroscopy
  • polymers

ASJC Scopus subject areas

  • Condensed Matter Physics


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