Laser induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopy through which elemental compositions of materials can be determined with little or no sample preparation. Small sample requirement as well as it capacity for rapid and real time analysis contributes significantly to the wider applicability of the technique. However, quantitative analysis of LIBS spectra remains a challenge and requires non-linear modeling technique that fully captures the complex interactions in the laser induced plasma and ultimately reduces the effect of self-absorption. Support vector regression (SVR) recently attracts significant attention in chemo-metrics due to its sound mathematical background and unique ability to model non-linear systems with reasonable degree of precision. This work proposes two novel techniques by which the performance of SVR can be improved for the quantitative analysis of LIBS spectra. The first technique, referred to as homogeneously hybridized support vector regression (HSVR), combines two SVR algorithms in which the output of the first algorithm serves as the input to the second algorithm while the second technique, referred to as internal reference preprocessing method (IRP), uses the spectra feature that is normalized with the emission line intensity which is not significantly affected by self-absorption. The hyper-parameters of the developed models are optimized using gravitational search algorithm (GSA). On the basis of root mean square error, GSA-HSVR-WIRP (without IRP) performs better than GSA-SVR-WIRP with over 75% performance improvement while GSA-HSVR-IRP performs better than GSA-SVR-IRP with over 95% performance improvement. The outcome of this work would be very useful for precise LIBS quantitative analysis and would eventually promote wide applicability of the technique.

中文翻译：

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LIBS光谱定量分析中增强支持向量回归化学计量学性能的新技术

激光诱导击穿光谱法（LIBS）是一种原子发射光谱法，通过它可以几乎不需要样品制备就可以确定材料的元素组成。少量的样品需求以及快速和实时分析的能力极大地促进了该技术的广泛应用。然而，LIBS光谱的定量分析仍然是一个挑战，需要非线性建模技术来完全捕获激光诱导等离子体中的复杂相互作用，并最终降低自吸收的影响。支持向量回归（SVR）最近在化学计量学中引起了广泛关注，因为它具有良好的数学背景和独特的以合理的精度对非线性系统进行建模的能力。这项工作提出了两种新颖的技术，通过这些技术可以提高SVR的性能，从而对LIBS光谱进行定量分析。第一种技术称为均质混合支持向量回归（HSVR），它结合了两种SVR算法，其中第一种算法的输出用作第二种算法的输入，而第二种技术称为内部参考预处理方法（IRP） ），使用由发射线强度归一化的光谱特征，该特征不会受到自吸收的明显影响。使用重力搜索算法（GSA）对已开发模型的超参数进行了优化。根据均方根误差，GSA-HSVR-WIRP（不带IRP）的性能优于GSA-SVR-WIRP，性能提高了75％以上，而GSA-HSVR-IRP的性能优于GSA-SVR-IRP，性能提高了95％以上。这项工作的结果对于精确的LIBS定量分析将非常有用，并将最终促进该技术的广泛应用。