Analytical Sciences

A Modified Moving-Window Partial Least-Squares Method by Coupling with Sampling Error Profile Analysis for Variable Selection in Near-Infrared Spectral Analysis

Wuye YANG,* Wenming WANG,* Ruoqiu ZHANG,* Feiyu ZHANG,* Yinran XIONG,* Ting WU,* Wanchao CHEN,** and Yiping DU*

*Shanghai Key Laboratory of Functional Materials Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China
**Institute of Edible Fungi, Shanghai Academy of Agriculture Sciences, National Engineering Research Center of Edible Fungi, Key Laboratory of Edible Fungi Resources and Utilization (South), Ministry of Agriculture, Shanghai 201403, P. R. China

In this study, a new variable selection method, named moving-window partial least-squares coupled with sampling error profile analysis (SEPA-MWPLS), is developed. With a moving window, moving-window partial least-squares (MWPLS) is used to find window intervals which show low residual sums of squares (RSS) of a calibration set. Sampling error profile analysis (SEPA) is a useful method based on Monte-Carlo Sampling and profile analysis for cross validation (CV). By combining MWPLS with SEPA, we can obtain more stable and reliable results. Besides, we simplify the plot of the RSS line so that it is easier to determine the informative intervals. In addition, a backward elimination strategy is used to optimize the combination of subintervals. The performance of SEPA-MWPLS was tested with two near-infrared (NIR) spectra datasets and was compared with PLS, MWPLS and Monte Carlo uninformative variable elimination (MC-UVE). The results show that SEPA-MWPLS can improve model performances significantly compared with MWPLS in the number of variables, root-mean-squared errors of CV, calibration and prediction (RMSECVs, RMSECs and RMSEPs). Meanwhile it also exhibits better performances than MC-UVE.

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