激光诱导击穿光谱定量化标定谱线自动选择方法

选择合适的特征谱线是以内标法对激光诱导击穿光谱进行定量化分析的前提，需要科研工作者精心分析和比较，往往耗费大量精力和时间，而且还不能保证结果最优。基于遗传算法为内标法提出了一种从原始光谱中自动选择分析线和参考线的方法，使用该方法从低合金钢样的LIBS光谱中为锰(Mn)、镍(Ni)、铬(Cr)、硅(Si)和铁(Fe)元素分别选择了用于内标法定量分析的分析线和参考线。优选得到的最优谱线为，Mn的分析线和相应的参考元素Fe的参考线分别是403.306 8和368.745 7 nm，Si的分析线和相应的参考元素Fe的参考线分别是288.157 7和427.176 1 nm，Cr的分析线和相应的参考元素Fe的参考线分别是286.510 0和272.753 9 nm，Ni的分析线和相应的参考元素Fe的参考线分别是352.453 6和358.698 5 nm。最后基于优选得到的谱线以内标法对这些元素分别进行了定量分析，结果证明，使用这种谱线选择方法能够自动从原始光谱中找到最优特征谱线，使内标法定量分析得到最好的结果。

Automatic Method for Selecting Characteristic Lines Based on Genetic Algorithm to Quantify Laser-Induced Breakdown Spectroscopy

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 re-searchers is time consuming and energy consuming,which cannot guarantee the best result.An automatic method to select ana-lytical 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 refer-ence 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 el-ements 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.