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首页> 外文期刊>Analytica chimica acta >Spectral regions selection to improve prediction ability of PLS models by changeable size moving window partial least squares adn searching combination moving window partial least squares
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Spectral regions selection to improve prediction ability of PLS models by changeable size moving window partial least squares adn searching combination moving window partial least squares

机译：通过可变大小的移动窗口最小二乘和搜索组合移动窗口最小二乘来选择光谱区域以提高PLS模型的预测能力。

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摘要

Changeable size moving window partial least squares (csmwpls)and searching combination moving window partial least squares (SCMWPLS)are proposed to search for an optimized spectral interval and an optimized combination of spectral regions from informative regions obtained by a previously proposed spectral interval selection method,moving windown partial least squares (MWPLSR)[Anal.Chem.74(2002)3555].The utilization of informative regins aims to construct better PLS models than those based on the whole spectral points.The purpose of CSMWPLS and SCMWPLS is to optimize the informative regions and their combination to further improve the prediction ability of the PLS models.The results of their application to an open-path (OP)/FT-IR spectra data set show that the proposed methods,especially SCMWPLS can find out an optimized combination,with which one can improve,ofter significantly,the performance of the corresponding PLS model,in terms of low prediction error,root mean square error of prediction (RMSEP)with the reasonable latent variable (LVs)number,comparing with the results obtained using whole spectra or direct combination of informative regions for a compound.Regions consisting of the combinations obtained can easily be explanined by the existence of IR absorption bands in those spectral regions.
机译：提出了可变大小的移动窗口偏最小二乘（csmwpls）和搜索组合移动窗口偏最小二乘（SCMWPLS），以从通过先前提出的频谱间隔选择方法获得的信息区域中搜索最佳光谱区间和光谱区域的最佳组合，移动窗偏最小二乘（MWPLSR）[Anal.Chem.74（2002）3555]。利用信息域旨在构建比基于整个光谱点的模型更好的PLS模型。CSMWPLS和SCMWPLS的目的是优化信息区域及其组合，以进一步提高PLS模型的预测能力。将其应用于开放路径（OP）/ FT-IR光谱数据集的结果表明，所提出的方法，特别是SCMWPLS可以找到优化的组合可以从较低的预测误差，预均方根误差等方面极大地改善相应PLS模型的性能与具有潜在隐变量（LVs）数的化合物（RMSEP）相比，与使用化合物的整个光谱或信息区域的直接组合所获得的结果相比较。由所获得的组合所组成的区域可以很容易地通过红外吸收带的存在来解释。那些光谱区域。

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