Variable selection in near infrared spectroscopy based on significance testing in partial least squares regression

被引：219

作者：

Westad, F

Martens, H

机构：

[1] MATFORSK, N-1430 As, Norway

[2] Tech Univ Denmark, Inst Biotechnol, DK-2800 Lyngby, Denmark

[3] Norwegian Univ Sci & Technol, Inst Phys Chem, N-7034 Trondheim, Norway

来源：

JOURNAL OF NEAR INFRARED SPECTROSCOPY | 2000年 / 8卷 / 02期

关键词：

NIR; partial least squares regression; multivariate calibration; cross-validation; jack-knife; uncertainty estimates; variable selection;

D O I：

10.1255/jnirs.271

中图分类号：

O69 [应用化学];

学科分类号：

081704 ;

摘要：

A jack-knife based method for variable selection in partial least squares regression is presented, The method is based on significance tests of model parameters, in this paper applied to regression coefficients. The method is tested on a near infrared (NIR) spectral data set recorded on beer samples, correlated to extract concentration and compared to other methods with known merit, The results show that the jack-knife based variable selection performs as well or better than other variable selection methods do. Furthermore, results show that the method is robust towards various cross-validation schemes (the number of segments and how they are chosen),

引用

页码：117 / 124

页数：8

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