A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra
A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra
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基于无信息变量消除的近红外光谱多元定标变量选择方法
DOI：
10.1016/j.chemolab.2007.10.001
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发表时间：
2008-02-15
影响因子：
3.9
通讯作者：
Shao, Xueguang
中科院分区：
文献类型：
--
作者：
Cai, Wensheng;Li, Yankun;Shao, Xueguang
变量（或波长）的选择在近红外光谱定量分析中起着重要的作用。基于蒙特卡罗（MC）和无信息变量剔除（UVE）的原理，提出了一种改进的UVE方法用于近红外光谱建模中的变量选择。该方法首先用随机选取的校准样本建立大量的模型，然后用模型中各变量对应系数的稳定性来评价模型中各变量的稳定性。稳定性差的变量被称为无信息变量并被删除。将该方法应用于烟草样品的近红外光谱数据集建模，并与UVE-PLS和传统的PLS方法进行了比较。结果表明，该方法能够从近红外光谱中选择出重要的波长，使预测结果更加稳健，定量分析更加准确。如果将小波压缩与该方法相结合，可以得到更简洁、更有效的模型。(C)2007 Elsevier B. V.保留所有权利。
Variable (or wavelength) selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra. A modified method of uninformative variable elimination (UVE) was proposed for variable selection in NIR spectral modeling based on the principle of Monte Carlo (MC) and UVE. The method builds a large number of models with randomly selected calibration samples at first, and then each variable is evaluated with a stability of the corresponding coefficients in these models. Variables with poor stability are known as uninformative variable and eliminated. The performance of the proposed method is compared with UVE-PLS and conventional PLS for modeling the NIR data sets of tobacco samples. Results show that the proposed method is able to select important wavelengths from the NIR spectra, and makes the prediction more robust and accurate in quantitative analysis. Furthermore, if wavelet compression is combined with the method, more parsimonious and efficient model can be obtained. (C) 2007 Elsevier B.V. All rights reserved.