Genetic algorithm optimisation combined with partial least squares regression and mutual information variable selection procedures in near-infrared quantitative analysis of cotton–viscose textiles

2007 ◽  
Vol 595 (1-2) ◽  
pp. 72-79 ◽  
Author(s):  
A. Durand ◽  
O. Devos ◽  
C. Ruckebusch ◽  
J.P. Huvenne
Keyword(s):  
Genetic Algorithm ◽  
Quantitative Analysis ◽  
Mutual Information ◽  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Least Squares Regression ◽  
Selection Procedures

Variable Selection in near Infrared Spectroscopy Based on Significance Testing in Partial Least Squares Regression

2000 ◽  
Vol 8 (2) ◽  
pp. 117-124 ◽  
Author(s):  
F. Westad ◽  
H. Martens
Keyword(s):  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Model Parameters ◽  
Least Squares Regression ◽  
Data Set ◽  
Number Of Segments ◽  
Better Than

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).

Sign in / Sign up

Export Citation Format

Share Document