在激光诱导击穿光谱定量分析中, 基线是LIBS光谱信号的重要组成部分, 由于受到实验参数设置及实验环境变化等影响, 导致等离子体的发射谱线呈现不同程度的基线漂移现象, 因此基线校正是光谱分析的重要环节。 现有算法如多项式拟合通常需要设定关键参数, 不具备自适应性。 由于激光诱导产生的等离子体光谱信号的特征峰具有明显的独立性和稀疏性, 以及基线具有低通特性, 因此在凸优化约束框架下, 提出了非参数化基线校正模型。 同时, 利用高效率的迭代算法来保证结果的收敛性。 首先对23个高合金钢样本的光谱信号进行基线校正, 然后以合金钢样本中的铬(Cr)元素为分析对象, 并选取11条分析线进行定量分析。 分别采用PLS和SVM定量模型进行训练和预测, 并且与传统方法相比较, 证明了所提出方法可以提高定量分析精度。

In the laser-induced breakdown spectroscopy (LIBS) quantitative analysis technique, the baseline is an important part of the LIBS signal. Due to the fluctuation of laser energy and inhomogeneity of sample surfaces, the phenomenon of plasma emission line drift is obvious. Thus, baseline correction is an essential part during the spectrum data preprocessing. However, the existing algorithms do not have adaptability, thus there usually needs to set some key parameters, such as the suitable fitting function and order. In this paper, baseline correction and spectral signal denoising method are proposed on the basis of the data characteristics, such as the independence and sparsity of the spectral peak as well as the low-pass signal feature of baseline, which forme non-parametric baseline correction model under convex optimization framework. Meanwhile, efficient iterative algorithm is used to ensure the convergence of the results. In this paper, the spectral signal of 23 high alloy steel samples was firstly calibrated, and then the Cr element in the samples of alloy steel was analyzed. Subsequently, 11 analytical lines were selected for quantitative analysis. To verify the effectiveness of the proposed method, we utilized partial least squares (PLS) and support vector machine (SVM) quantitative model for training and prediction respectively. Compared with the traditional methods, the proposed method has a better performance in improving the quantitative analysis accuracy.