Multivariate data analysis as a fast tool in evaluation of solid state phenomena

Abstract

A thorough understanding of solid state properties is of growing importance. It is often necessary to apply multiple techniques offering complementary information to fully understand the solid state behavior of a given compound and the relations between various polymorphic forms. The vast amount of information generated can be overwhelming and the need for more effective data analysis tools is well recognized. The aim of this study was to investigate the use of multivariate data analysis, in particular principal component analysis (PCA), for fast analysis of solid state information. The data sets analyzed covered dehydration phenomena of a set of hydrates followed by variable temperature X-ray powder diffractometry and Raman spectroscopy and the crystallization of amorphous lactose monitored by Raman spectroscopy. Identification of different transitional states upon the dehydration enabled the molecular level interpretation of the structural changes related to the loss of water, as well as interpretation of the phenomena related to the crystallization. The critical temperatures or critical time points were identified easily using the principal component analysis. The variables (diffraction angles or wavenumbers) that changed could be identified by the careful interpretation of the loadings plots. The PCA approach provides an effective tool for fast screening of solid state information.