ABSTRACT
The field of chemometrics has developed to facilitate the transfer of statistical techniques to the chemical problems that require them. This chapter emphasises that chemometrics is a chemical discipline because the interpretive power of the statistical techniques can be fully exploited only when a chemical context is present. The rationale for using pattern recognition to examine large multivariate data bases is to enhance human understanding of the multidimensional information contained in the data. The pattern recognition approach consists of two phases; exploratory data analysis and applied pattern recognition. Exploratory data analysis is designed to uncover three main aspects of the data: anomalous samples or measurements, significant relationships among the measured variables, significant relationships or groupings among the samples. Exploratory data analysis is an iterative process in which a wide variety of tools are employed. The three primary tools used in this approach are factor analysis, principal component analysis, and cluster analysis.
