| 光谱学与光谱分析 |
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| Automatic Method for Selecting Characteristic Lines Based on Genetic Algorithm to Quantify Laser-Induced Breakdown Spectroscopy |
| KONG Hai-yang1, 2, 3, SUN Lan-xiang1, 3*, HU Jing-tao1, 3, ZHANG Peng1, 2, 3 |
1. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Key Laboratory of Networked Control System, Chinese Academy of Sciences, Shenyang 110016, China |
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Abstract Selecting proper characteristic lines from enormous spectral intensities is crucially important to implement quantitative analysis of Laser-Induced Breakdown Spectroscopy using internal standard method. Manual selecting of characteristic lines by researchers is time consuming and energy consuming, which cannot guarantee the best result. An automatic method to select analytical and reference lines for internal standard method from the original spectra based on Genetic Algorithm was proposed in this paper. This method was utilized to select analytical and reference lines for internal standard methods from LIBS of Mn, Ni, Cr, Si and Fe of low alloy steels. The optimal characteristic lines optimized by this method were the analytical line 403.306 8 nm of Mn and the corresponding reference line 368.745 7 nm of Fe, the analytical line 288.157 7 nm of Si and the corresponding reference line 427.176 1 nm of Fe, the analytical line 286.510 0 nm of Cr and the corresponding reference line 272.753 9 nm of Fe and the analytical line 352.453 6 nm of Ni and the corresponding reference line 358.698 5 nm of Fe, respectively. Then these elements were quantified by the internal standard method using these selected lines. The results showed that this proposed method for selecting characteristic lines can automatically select the optimal analytical and reference lines and could guarantee the best quantitative result obtained by internal standard method.
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Received: 2015-01-17
Accepted: 2015-04-27
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Corresponding Authors:
SUN Lan-xiang
E-mail: sunlanxiang@sia.cn
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