Removing uncertain variables based on ensemble partial least squares

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.