激光诱导击穿光谱中分析谱线的自适应选择方法

Adaptive Selection Method for Analytical Lines in Laser-Induced Breakdown Spectra

激光诱导击穿光谱技术具有实时在线、非接触式测量和无损分析等优点,在物质检测领域得到了广泛应用,选择合适的分析谱线是其取得良好检测效果的重要基础。结合遗传算法(GA)的全局优化能力和粒子群算法(PSO)的局部搜索能力,提出了一种从激光诱导击穿光谱的原始光谱数据中自适应选择分析谱线与内标谱线的方法,利用该方法选择的分析谱线与内标谱线对铝合金中4种主要非铝元素(Mg、Mn、Si和Fe)进行定量分析,得到的拟合优度均值为0.972,均方根误差均值为0.35%,相对标准差均值为3.53%,最后遍历其他所有分析谱线进行定量分析,并对比它们的定标性能。结果表明,利用PSO-GA搜索优化得到的分析谱线与内标谱线较PSO、GA算法获得的谱线更优。

Laser-induced breakdown spectroscopy is widely used in the material detection field because of its advantages,including online noncontact measurement and non-destructive analysis.Selecting proper analytical lines is an important prerequisite for achieving a good detection effect.This study proposed a method for adaptively selecting analytical and internal standard lines from the original spectral data of LIBS based on the global optimization ability of the genetic algorithm(GA)and the local search ability of the particle swarm optimization(PSO)algorithm.We quantitatively analyzed four major non-aluminum elements(i.e.,Mg,Mn,Si,and Fe)in aluminum alloys using the analytical and internal standard lines selected using this method.The mean values of the goodness of fit,root mean square error,and relative standard deviation are 0.972,0.35%,and 3.53%,respectively.The results obtained by traversing all other analytical lines for a quantitative analysis and comparing their calibration performances show that the analytical and internal standard lines obtained by the PSO-GA search optimization are optimal analytical spectral lines under current experimental conditions.