Assessment of chromatographic peak purity of drugs by multivariate analysis of diode-array and mass spectrometric data

Abstract

Numerous multivariate chemometric approaches have been developed for LC—UV data acquired using a diode-array detector (DAD), but these methods have not been widely exploited for LC—MS data. Principal component analysis (PCA) and subsequent axis rotation within the reduced factor space are assessed for LC—DAD and LC—MS data as approaches for estimating the number of components (i.e. the rank of the data) under a single chromatographic peak for compounds whose UV-spectra are very similar. Multivariate techniques for LC—DAD data are shown to suffer from inherent limitations of sensitivity for the minor components. The novel technique in LC—MS of plotting the rotated PCA data in two-dimensional factor space generates characteristic ion clusters, giving a visual criterion of peak purity. Single ion chromatograms produced subsequently confirm the profile of each coeluting component and give evidence of the degree of peak overlap. The application of this new chemometric technique to the detection of low levels of coeluting impurities by LC—MS is discussed as a novel approach for the validation of LC separations in pharmaceutical research and development.