A novel baseline correction method using convex optimization framework in laser-induced breakdown spectroscopy quantitative analysis
A novel baseline correction method using convex optimization framework in laser-induced breakdown spectroscopy quantitative analysis
复制标题
激光诱导击穿光谱定量分析中使用凸优化框架的新型基线校正方法
DOI：
10.1016/j.sab.2017.10.014
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发表时间：
2017-12
期刊：
影响因子：
--
通讯作者：
Xun Yu
中科院分区：
文献类型：
--
作者：
Cancan Yi;Yong Lv;Han Xiao;Ke Ke;Xun Yu
在激光诱导击穿光谱（LIBS）定量分析技术中，基线校正是LIBS数据预处理的重要环节。由于激光能量的波动、样品表面的不均匀性以及背景噪声等因素的影响，导致了基线漂移现象的广泛存在，引起了许多研究者的兴趣。目前流行的算法大多需要预先设定一些关键参数，如合适的样条函数、拟合阶数等，不具有自适应性。针对LIBS光谱峰值稀疏、基线低通滤波等特点，研究了一种新的基线校正和光谱数据去噪方法。该方法利用凸优化方法建立非参数基线校正模型。同时引入非对称惩罚函数，提高LIBS信号的信噪比，提高重建精度。在优化过程中采用了一种高效的迭代算法，保证了算法的收敛性。为了验证所提出的方法，铬（Cr），锰（Mn）和镍（Ni）的23个认证的高合金钢样品中的浓度分析进行了评估，使用定量模型与偏最小二乘法（PLS）和支持向量机（SVM）。由于没有样本组成的先验知识和数学假设，与其他方法相比，本文提出的方法在定量分析时具有更好的准确性，充分体现了其自适应能力。
For laser-induced breakdown spectroscopy (LIBS) quantitative analysis technique, baseline correction is an essential part for the LIBS data preprocessing. As the widely existing cases, the phenomenon of baseline drift is generated by the fluctuation of laser energy, inhomogeneity of sample surfaces and the background noise, which has aroused the interest of many researchers. Most of the prevalent algorithms usually need to preset some key parameters, such as the suitable spline function and the fitting order, thus do not have adaptability. Based on the characteristics of LIBS, such as the sparsity of spectral peaks and the low-pass filtered feature of baseline, a novel baseline correction and spectral data denoising method is studied in this paper. The improved technology utilizes convex optimization scheme to form a non-parametric baseline correction model. Meanwhile, asymmetric punish function is conducted to enhance signal-noise ratio (SNR) of the LIBS signal and improve reconstruction precision. Furthermore, an efficient iterative algorithm is applied to the optimization process, so as to ensure the convergence of this algorithm. To validate the proposed method, the concentration analysis of Chromium (Cr),Manganese (Mn) and Nickel (Ni) contained in 23 certified high alloy steel samples is assessed by using quantitative models with Partial Least Squares (PLS) and Support Vector Machine (SVM). Because there is no prior knowledge of sample composition and mathematical hypothesis, compared with other methods, the method proposed in this paper has better accuracy in quantitative analysis, and fully reflects its adaptive ability.
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