A review of variable selection methods in Partial Least Squares Regression

Variables selection methods in near-infrared spectroscopy.

icoshift: A versatile tool for the rapid alignment of 1D NMR spectra

Near infrared spectroscopy: A mature analytical technique with new perspectives - A review.

This paper provides a practical guide to variable selection in chemometrics with a focus on regression-based calibration models. Several approaches, such as genetic algorithms (GAs), jack-knifing, forward selection, etc., are explained; it is also explained how to choose between different kinds of variable selection methods. The emphasis in this paper is on how to use variable selection in practice and avoid the most common pitfalls. Copyright © 2010 John Wiley & Sons, Ltd.

Variable selection in regression—a tutorial

A new graphically oriented local modeling procedure called interval partial least-squares (i PLS) is presented for use on spectral data. The i PLS method is compared to full-spectrum partial least-squares and the variable selection methods principal variables (PV), forward stepwise selection (FSS), and recursively weighted regression (RWR). The methods are tested on a near-infrared (NIR) spectral data set recorded on 60 beer samples correlated to original extract concentration. The error of the full-spectrum correlation model between NIR and original extract concentration was reduced by a factor of 4 with the use of i PLS (r=0.998, and root mean square error of prediction equal to 0.17% plato), and the graphic output contributed to the interpretation of the chemical system under observation. The other methods tested gave a comparable reduction in the prediction error but suffered from the interpretation advantage of the graphic interface. The intervals chosen by i PLS cover both the variables found by FSS and all possible combinations as well as the variables found by PV and RWR, and i PLS is still able to utilize the first-order advantage. Index Headings: Interval PLS; Variable selection; NIR, Principal variables; Forward stepwise selection; Recursively weighted regression; Beer; Extract.

A. Saudland

Papers

Interval Partial Least-Squares Regression (iPLS): A Comparative Chemometric Study with an Example from Near-Infrared Spectroscopy