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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 53, Iss. 4 — Feb. 1, 2014
  • pp: 544–552

Classification of steel materials by laser-induced breakdown spectroscopy coupled with support vector machines

Long Liang, Tianlong Zhang, Kang Wang, Hongsheng Tang, Xiaofeng Yang, Xiaoqin Zhu, Yixiang Duan, and Hua Li  »View Author Affiliations

Applied Optics, Vol. 53, Issue 4, pp. 544-552 (2014)

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The feasibility of steel materials classification by support vector machines (SVMs), in combination with laser-induced breakdown spectroscopy (LIBS) technology, was investigated. Multi-classification methods based on SVM, the one-against-all and the one-against-one models, and a combination model, are applied to classify nine types of round steel. Due to the inhomogeneity of steel composition, the data obtained using the one-against-all and one-against-one models were ambiguous and difficult to discriminate; whereas, the combination model, was able to successfully distinguish most of the ambiguous data and control the computation cost within an acceptable range. The studies presented here demonstrate that LIBS–SVM is a useful technique for the identification and discrimination of steel materials, and would be very well-suited for process analysis in the steelmaking industry.

© 2014 Optical Society of America

OCIS Codes
(140.3440) Lasers and laser optics : Laser-induced breakdown
(280.1545) Remote sensing and sensors : Chemical analysis
(300.6365) Spectroscopy : Spectroscopy, laser induced breakdown

ToC Category:
Lasers and Laser Optics

Original Manuscript: October 17, 2013
Revised Manuscript: December 5, 2013
Manuscript Accepted: December 9, 2013
Published: January 23, 2014

Long Liang, Tianlong Zhang, Kang Wang, Hongsheng Tang, Xiaofeng Yang, Xiaoqin Zhu, Yixiang Duan, and Hua Li, "Classification of steel materials by laser-induced breakdown spectroscopy coupled with support vector machines," Appl. Opt. 53, 544-552 (2014)

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