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- W2022108380 abstract "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." @default.
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- W2022108380 date "2007-08-01" @default.
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- W2022108380 title "Removing uncertain variables based on ensemble partial least squares" @default.
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- W2022108380 doi "https://doi.org/10.1016/j.aca.2007.07.023" @default.
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