Chemometrics

Abstract

Chemometrics has been defined as the chemical discipline that uses mathematical and statistical methods to design or select optimal measurement procedures and experiments and to provide maximum chemical information by analyzing chemical data. The definition, historical development, salient methods, and illustrative applications of chemometrics are given. Methods of pattern recognition for exploratory data analysis are discussed, including techniques of scaling, feature weighting, principal components analysis, cluster analysis, classification methods, such as linear learning machine, k‐nearest neighbor, and SIMCA, and modeling methods, such as partial least squares path modeling. Methods for multivariate calibration and resolution are discussed including the generalized standard addition method (GSAM) and the self‐modeling curve resolution approach. Methods to optimize experimental performance, including simplex optimization, are outlined. Illustrative examples of the application of chemometrics in forensic chemistry, environmental chemistry, clinical chemistry, sensor design, and industrial process control are presented.