Chemometrics and modeling

Abstract

Chemometrics is a chemical discipline in which mathematical and statistical techniques are applied to design experiments or to analyze chemical data. An important part of chemometrics is modeling, in which one tries to relate two or more characteristics in such a way that the obtained model represents reality as closely as possible. In this article some less known but useful regression methods such as orthogonal least squares, inverse and robust regression are introduced and compared with the well-known classical least squares regression method. Genetic algorithms are described as a means of carrying out feature selection for multivariate regression. Regression methods such as principal component regression and partial least squares are introduced as well as the use of N-way principal components.