A Nonlinearized Multivariate Dominant Factor-Based Partial Least Squares (PLS) Model for Coal Analysis by Using Laser-Induced Breakdown Spectroscopy
A Nonlinearized Multivariate Dominant Factor-Based Partial Least Squares (PLS) Model for Coal Analysis by Using Laser-Induced Breakdown Spectroscopy
复制标题
使用激光诱导击穿光谱进行煤炭分析的基于非线性多元主导因子的偏最小二乘 (PLS) 模型
DOI：
10.1366/11-06393
复制
发表时间：
2013-03-01
影响因子：
3.5
通讯作者：
Ni, Weidou
中科院分区：
文献类型：
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作者：
Feng, Jie;Wang, Zhe;Ni, Weidou
将非线性多元主导因子偏最小二乘（PLS）模型应用于煤中元素浓度的测量。对于烟煤中C含量的测定，利用煤中主要元素多条特征线的强度，构建综合主导因子，提供主要含量结果。随后应用的第二PLS将通过使用整个光谱信息来进一步校正模型结果。在主导因子提取中，将基于物理机制的谱线强度非线性变换嵌入到线性偏最小二乘法中，以更有效、更准确地描述非线性自吸收和元素间干扰。根据自吸收的经验表达式和泰勒展开式，利用C原子和离子谱线强度的非线性变换来模拟自吸收。然后，考虑到C与O和N粒子可能的复合，将其他元素O和N的线强度考虑为元素间干涉。在多元主导因子的构建中考虑了激光诱导击穿光谱（LIBS）分析煤的特点。该模型取得了更好的预测性能比传统的PLS。与我们以前的，已经改进的主导因素的PLS模型相比，本PLS模型获得了相同的校准质量，同时降低预测的均方根误差（RMSEP）从4.47%到3.77%。此外，采用留一法交叉验证和L曲线法代替最小RMSEP准则，避免了主成分个数的过拟合问题，对校正和预测样本的不同分割也表现出了较好的效果，证明了PLS模型的稳健性。
A nonlinearized multivariate dominant factor based partial least-squares (PLS) model was applied to coal elemental concentration measurement. For C concentration determination in bituminous coal, the intensities of multiple characteristic lines of the main elements in coal were applied to construct a comprehensive dominant factor that would provide main concentration results. A secondary PLS thereafter applied would further correct the model results by using the entire spectral information. In the dominant factor extraction, nonlinear transformation of line intensities (based on physical mechanisms) was embedded in the linear PLS to describe nonlinear self-absorption and inter-element interference more effectively and accurately. According to the empirical expression of self-absorption and Taylor expansion, nonlinear transformations of atomic and ionic line intensities of C were utilized to model self-absorption. Then, the line intensities of other elements, O and N, were taken into account for inter-element interference, considering the possible recombination of C with O and N particles. The specialty of coal analysis by using laser-induced breakdown spectroscopy (LIBS) was also discussed and considered in the multivariate dominant factor construction. The proposed model achieved a much better prediction performance than conventional PLS. Compared with our previous, already improved dominant factor based PLS model, the present PLS model obtained the same calibration quality while decreasing the root mean square error of prediction (RMSEP) from 4.47 to 3.77%. Furthermore, with the leave-one-out cross-validation and L-curve methods, which avoid the overfitting issue in determining the number of principal components instead of minimum RMSEP criteria, the present PLS model also showed better performance for different splits of calibration and prediction samples, proving the robustness of the present PLS model.