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Improved Variable Reduction in Partial Least Squares Modelling by Global-Minimum Error Reproducible Uninformative-Variable Elimination

delete2025-11-29
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OA
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J
Jan P.M. Andries
G
Gerjen H. Tinnevelt
Y
Yvan Vander Heyden
DOI：10.1016/j.chemolab.2025.105603delete
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Abstract

Abstract

En 中文
• A new and reproducible UVE method for PLS1, called GME-RUVE, is proposed. • Variable set is selected at the global minimum or at the critical RMSECV. • GME-RUVE has better selective and predictive abilities than JK-PLSR, UVE and GME-UVE. • Variables selected by GME-RUVE have a chemical meaning.
Keywords:
Variable elimination
Partial Least Squares Regression (PLSR)
Global-Minimum Error Reproducible Uninformative-Variable Elimination (GME-RUVE)
Jack-knife based PLS regression (JK-PLSR)
Global RMSECV minimum
critical RMSECV
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Journal

Chemometrics and Intelligent Laboratory Systems cover
Chemometrics and Intelligent Laboratory Systems
IF:
3.8
Papers:
4.6K
Citations:
1.2W

Organization

V
vrije universiteit brussel-vub
Scholars:
1
Papers: 1
Citations: 0
I
institute for molecules and materials
Scholars:
44
Papers: 18
Citations: 0
A
Avans University of Applied Sciences
Scholars:
16
Papers: 10
Citations: 254
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