Variable selection in near infrared spectroscopy based on significance testing in partial least squares regression
Publication details
Journal : Journal of Near Infrared Spectroscopy , vol. 8 , p. 117–124 , 2000
International Standard Numbers
:
Printed
:
0967-0335
Electronic
:
1751-6552
Publication type : Academic article
Issue : 2
Links
:
DOI
:
doi.org/10.1255/jnirs.271
If you have questions about the publication, you may contact Nofima’s Chief Librarian.
Kjetil Aune
Chief Librarian
kjetil.aune@nofima.no
Summary
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).