Enter the Matrix: Improving the Interpretation of Separations Data Using Chemometrics in Analytical Investigations

Abstract

This article describes a compendium of chemometrics applications in separation science to demonstrate the importance and synergy of data handling. We review the main points that must be raised when using multivariate techniques and list references for further reading. With examples in food science, the case studies comprise of applications of pattern recognition (principal component analysis, PCA), regression (partial least squares, PLS), and classification methods (soft independent modelling of class analogies, SIMCA).