Monitoring polymorphic transformations by using in situ Raman hyperspectral imaging and image multiset analysis

Abstract

Polymorphism is often encountered in many crystalline compounds. To control the quality of the products, it is relevant knowing the potential presence of polymorph transformations induced by different agents, such as light exposure or temperature changes. Raman images offer a great potential to identify polymorphs involved in a process and to accurately describe this kind of solid-state transformation in the surface scanned.As a way of example, this work proposes the use of multiset analysis on the series of Raman hyperspectral images acquired during a thermal induced transformation of carbamazepine as the optimal way to extract useful information about polymorphic or any other kind of dynamic transformation among process compounds. Image multiset analysis, performed by using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS), will furnish pure spectra and distribution maps of the compounds involved in the process and, hence, will allow the identification of polymorphs and, more important, the description of the process evolution at a global and local (pixel) level. Thus, process will be defined from a spatial point of view and by means of a set of global process profiles dependent on the process control variable. The results obtained confirm the power of this methodology and show the crucial role of the spatial information contained in the image (absent in conventional spectroscopy) for a correct process description.