Applications of Chemometrics for Characterization of Macromolecules

Abstract

The possibilities of employing methods of chemometrics in order to characterize macromolecules are described. The review has been limited to chemometric methods concerning multivariate data analysis. Principal component analysis (PCA) has shown to be very useful for pattern recognition problems arising from chromatographic and spectroscopic data. An example of using a classification technique, SIMCA (Soft Independent Modelling of Class Analogy), as a product control method is presented. The suitability of Partial Least Squares (PLS) for relating data of different natures, e.g. chemical data with biological data, is discussed. Moreover, examples ranging from spectroscopic determinations to QSAR (Quantitative Structure Activity Relationships) are illustrated.