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Updated: Jan 5, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
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A Modified Moving-Window Partial Least-Squares Method by Coupling with Sampling Error Profile Analysis for Variable

Wuye Yang1, Wenming Wang1, Ruoqiu Zhang1

  • 1Shanghai Key Laboratory of Functional Materials Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, China.

Analytical Sciences : the International Journal of the Japan Society for Analytical Chemistry
|October 16, 2019
PubMed
Summary

A new variable selection method, SEPA-MWPLS, improves near-infrared spectral analysis by combining moving-window partial least-squares with sampling error profile analysis for more reliable results.

Keywords:
Moving-window partial least-squaresnear infrared spectroscopysampling error profile analysisvariable selection

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Area of Science:

  • Chemometrics
  • Spectroscopy
  • Data Analysis

Background:

  • Variable selection is crucial for building robust chemometric models.
  • Existing methods like MWPLS and MC-UVE have limitations in stability and reliability.

Purpose of the Study:

  • To develop a novel variable selection method, SEPA-MWPLS, for enhanced chemometric modeling.
  • To improve the accuracy and reliability of near-infrared (NIR) spectral data analysis.

Main Methods:

  • Developed SEPA-MWPLS by integrating moving-window partial least-squares (MWPLS) with sampling error profile analysis (SEPA).
  • Utilized Monte-Carlo Sampling and profile analysis for cross-validation (CV).
  • Incorporated a backward elimination strategy for optimizing subinterval combinations.

Main Results:

  • SEPA-MWPLS significantly improved model performance compared to MWPLS.
  • Achieved better results in terms of variable reduction and prediction accuracy (RMSECVs, RMSECs, RMSEPs).
  • Outperformed Monte Carlo uninformative variable elimination (MC-UVE) in tested NIR datasets.

Conclusions:

  • SEPA-MWPLS offers a more stable and reliable approach to variable selection in chemometrics.
  • The method enhances the performance of NIR spectral data analysis.
  • Simplifies informative interval determination and optimizes model building.