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Proceedings Paper

A method derived from genetic algorithm, principal component analysis and artificial neural networks to enhance classification capability of laser-Induced breakdown spectroscopy
Author(s): P. Zhang; L. X. Sun; H. Y. Kong; H. B. Yu; M. T. Guo; P. Zeng
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Paper Abstract

Selection of characteristic lines is a critical work for both qualitative and quantitative analysis of laser-induced breakdown spectroscopy; it usually needs a lot of time and effort. A novel method combining genetic algorithm, principal component analysis and artificial neural networks (GA-PCA-ANN) is proposed to automatically extract the characteristic spectral segments from the original spectra, with ample feature information and less interference. On the basis of this method, three selection manners: selecting the whole spectral range, optimizing a fixed-length segment and optimizing several non-fixed-length sub-segments were analyzed; and their classification results of steel samples were compared. It is proved that selecting a fixed-length segment with an appropriate segment length achieves better results than selecting the whole spectral range; and selecting several non-fixed-length sub-segments obtains the best result with smallest amount of data. The proposed GA-PCA-ANN method can reduce the workload of analysis, the usage of bandwidth and cost of spectrometers. As a result, it can enhance the classification capability of laser-induced breakdown spectroscopy.

Paper Details

Date Published: 24 October 2017
PDF: 10 pages
Proc. SPIE 10461, AOPC 2017: Optical Spectroscopy and Imaging, 1046107 (24 October 2017); doi: 10.1117/12.2281493
Show Author Affiliations
P. Zhang, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Key Lab. of Networked Control System (China)
L. X. Sun, Shenyang Institute of Automation (China)
Key Lab. of Networked Control System (China)
H. Y. Kong, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
H. B. Yu, Shenyang Institute of Automation (China)
Key Lab. of Networked Control System (China)
M. T. Guo, Shenyang Institute of Automation (China)
Key Lab. of Networked Control System (China)
P. Zeng, Shenyang Institute of Automation (China)
Key Lab. of Networked Control System (China)


Published in SPIE Proceedings Vol. 10461:
AOPC 2017: Optical Spectroscopy and Imaging
Jin Yu; Zhe Wang; Wei Hang; Bing Zhao; Xiandeng Hou; Mengxia Xie; Tsutomu Shimura, Editor(s)

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