Quantitative analysis of steel samples by laser-induced-breakdown spectroscopy with wavelet-packet-based relevance vector machines,Journal of Analytical Atomic Spectrometry

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Quantitative analysis of steel samples by laser-induced-breakdown spectroscopy with wavelet-packet-based relevance vector machines
Journal of Analytical Atomic Spectrometry ( IF 3.1 ) Pub Date : 2018-04-19 00:00:00 , DOI: 10.1039/c7ja00421d
Shichen Xie _{1,

2,

3,

4,

5} , Tao Xu _{1,

2,

3,

4,

5} , Guanghui Niu _{5,

6,

7,

8} , Wenlong Liao _{5,

7,

8,

9} , Qinyu Lin _{1,

2,

3,

4,

5} , Yixiang Duan _{1,

2,

3,

4,

5}

Affiliation

Laser-induced breakdown spectroscopy (LIBS) has been gradually adopted as a quantitative technique for metallurgy analysis in recent years. However, the accuracy and efficiency of quantitative analysis is still a challenge. In this work, a novel method is proposed to achieve precise in situ composition prediction, based on wavelet packet transform (WPT) and relevance vector machine (RVM). We discuss the difference in LIBS spectral features extracted by the traditional method and WPT, as well as the absolute error of prediction and the mean relative error used as measurement criteria. The analysis results showed that the WPT method of extracting spectral features was more effective than the traditional method. Besides, for predicting the elemental compositions of the regression model, a better performance was obtained using RVM with a modified Laplacian kernel function (MRVM). The mean values of the root mean square error prediction (RMSEP) of MRVM, the calibration curve, RVM, and support vector machine were 0.159, 0.210, 0.303 and 0.179, respectively. Analysis results demonstrated that MRVM possessed superior efficiency, generalization ability and robustness for accurate and reliable compositional prediction. We thought that the proposed algorithm combined with LIBS can be used in real-time composition monitoring of steel samples.

中文翻译：

基于小波包相关矢量的激光诱导击穿光谱法对钢样品进行定量分析

近年来，激光诱导击穿光谱法（LIBS）已逐渐被用作冶金分析的定量技术。但是，定量分析的准确性和效率仍然是一个挑战。在这项工作中，提出了一种新颖的方法来实现精确的原位基于小波包变换（WPT）和相关向量机（RVM）的合成预测。我们讨论了传统方法和WPT提取的LIBS光谱特征的差异，以及预测的绝对误差和用作测量标准的平均相对误差。分析结果表明，WPT方法提取光谱特征比传统方法更为有效。此外，为了预测回归模型的元素组成，使用具有改进的拉普拉斯内核函数（MRVM）的RVM可以获得更好的性能。MRVM，校准曲线，RVM和支持向量机的均方根误差预测（RMSEP）的平均值分别为0.159、0.210、0.303和0.179。分析结果表明，MRVM具有较高的效率，准确和可靠的成分预测的泛化能力和鲁棒性。我们认为所提出的算法与LIBS相结合可以用于钢铁样品的实时成分监测。

更新日期：2018-04-19

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