Elimination of Uninformative Variables for Multivariate Calibration

TLDR

A new method for eliminating uninformative variables from multivariate data sets is proposed. The method adds artificial noise variables, derives a closed‑form PLS/PCR model, removes experimental variables whose importance is no greater than the noise variables according to a b‑coefficient criterion, and evaluates performance on simulated data. Practical aspects are discussed on experimentally obtained near‑IR data sets, and the study concludes that eliminating uninformative variables can improve predictive ability.

Abstract

A new method for the elimination of uninformative variables in multivariate data sets is proposed. To achieve this, artificial (noise) variables are added and a closed form of the PLS or PCR model is obtained for the data set containing the experimental and the artificial variables. The experimental variables that do not have more importance than the artificial variables, as judged from a criterion based on the b coefficients, are eliminated. The performance of the method is evaluated on simulated data. Practical aspects are discussed on experimentally obtained near-IR data sets. It is concluded that the elimination of uninformative variables can improve predictive ability.