Elastic Net Grouping Variable Selection Combined with Partial Least Squares Regression (EN-PLSR) for the Analysis of Strongly Multi-collinear Spectroscopic Data

Authors:
  • HLHong-Dong Li
    Hong-Dong Li
  • DCDong-Sheng Cao
    Dong-Sheng Cao
  • YLYi-Zeng Liang
    Yi-Zeng Liang
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Abstract
In this paper a novel wavelength region selection algorithm, called elastic net grouping variable selection combined with partial least squares regression (EN-PLSR), is proposed for multi-component spectral data analysis. The EN-PLSR algorithm can automatically select successive strongly correlated prediction variable groups related to the response variable using two steps. First, a portion of the correlated predictors are selected and divided into subgroups by means of the grouping effect of elastic net estimation. Then, a recursive leave-one-group-out strategy is employed to further shrink the variable groups in terms of the root mean square error of cross-validation (RMSECV) criterion. The performance of the algorithm with real near-infrared (NIR) spectroscopic data sets shows that the EN-PLSR algorithm is competitive with full-spectrum PLS and moving window partial least squares (MWPLS) regression methods and it is suitable for use with strongly correlated spectroscopic data.