Combination of heuristic optimal partner bands for variable selection in near-infrared spectral analysis
Combination of heuristic optimal partner bands for variable selection in near-infrared spectral analysis
复制标题
近红外光谱分析中变量选择的启发式最佳合作带组合
DOI：
10.1002/cem.2971
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发表时间：
2018-11-01
影响因子：
2.4
通讯作者：
Shao, Xueguang
中科院分区：
文献类型：
--
作者：
Zhang, Jin;Cui, Xiaoyu;Shao, Xueguang
变量选择在近红外光谱分析中起着至关重要的作用。提出了一种基于连续投影算法（SPA）和最佳配对波长组合（OPWC）原理的近红外光谱分析变量选择方法。该方法通过集对分析确定了一组具有足够独立性的节点变量，并定义了具有一定宽度的候选变量带。利用OPWC方法建立偏最小二乘回归模型，对波段间的协同效应进行了评价。利用近红外光谱数据集对片剂、玉米和土壤进行了分析，并与SPA、OPWC、随机化检验、竞争性自适应加权抽样和蒙特卡罗无信息变量剔除等方法进行了比较。结果表明，该方法可以选择具有协同效应的信息变量波段，改善了定量分析模型。
Variable selection plays a critical role in the analysis of near-infrared (NIR) spectra. A method for variable selection based on the principle of the successive projection algorithm (SPA) and optimal partner wavelength combination (OPWC) was proposed for NIR spectral analysis. The method determines a number of knot variables with sufficient independence by SPA, and candidate variable bands with a definite width are defined. The cooperative effect of the bands is then evaluated with the partial least squares regression model by using the method of OPWC. The performance of the proposed method was compared with those of SPA, OPWC, randomization test, competitive adaptive reweighted sampling, and Monte Carlo uninformative variable elimination by using NIR datasets for pharmaceutical tablets, corn, and soil. The results show that the proposed method can select informative variable bands with a cooperative effect and improves the model for quantitative analysis.