Removing uncertain variables based on ensemble partial least squares.
Removing uncertain variables based on ensemble partial least squares.
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DOI：
10.1016/j.aca.2007.07.023
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发表时间：
2007-08
影响因子：
6.2
通讯作者：
Da Chen;W. Cai;X. Shao
中科院分区：
文献类型：
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作者：
Da Chen;W. Cai;X. Shao
提出了一种基于集成偏最小二乘的不确定变量去除策略（RUV-EPLS）。该策略利用稳定性准则对偏最小二乘回归系数的不确定性进行评估，剔除回归系数中不确定性较大的变量。然后，一个新的EPLS模型的其余变量的构建。为了合理控制RUV-EPLS中PLS成员模型的质量，采用了基于F检验的客观准则，使RUV-EPLS在实践中易于操作。为了验证该策略的有效性和普适性，将其应用于两组不同的近红外（NIR）光谱。非常有趣的是，发现RUV-EPLS对异常值不像许多其他校准方法那样敏感，并且所选变量确实已知为相应化合物的信息，这导致可靠且高质量的校准模型。研究表明，RUV-EPLS方法是有价值的，以提高稳定性和预测能力的多元校正涉及复杂的矩阵，可能包含少量的离群值。
A strategy, named as removing uncertain variables based on ensemble partial least squares (RUV-EPLS), was proposed. In this strategy, the uncertainty in PLS regression coefficients is evaluated by the criterion of stability, and the variables whose regression coefficients carry a relatively large uncertainty are eliminated. Then, a new EPLS model with the remaining variables is constructed. To reasonably control the quality of the PLS member models in the RUV-EPLS, an objective criterion based on the F-test is used, which makes the RUV-EPLS convenient to perform in practice. To validate the effectiveness and universality of the strategy, it was applied to two different sets of near-infrared (NIR) spectra. It is of great interest to be found that the RUV-EPLS is not so sensitive to the outliers as many other calibration methods, and the selected variables are indeed known to be informative for corresponding compounds, which results in a reliable and high-quality calibration model. The study reveals that the RUV-EPLS method is of value to improve stability and predictive ability of multivariate calibration involving complex matrices that may contain a small number of outliers.