Deconvolution and spectral clean-up of two-component mixtures by factor analysis of gas chromatographic–mass spectrometric data

Abstract

A method is proposed for the deconvolution of GC–MS data. It is applied to two datasets, both of mixtures of the closely eluting and spectroscopically similar compounds salbutamol and clenbuterol. In one set the components are well resolved, whereas in the other, one peak is broadened so that there is complete overlap. After visually looking at the data using principal components plots and baseline correction, the first step is to choose masses characteristic of each component in the dataset. These are then used to estimate elution profiles. Forty masses, ranked in order of significance according to the value of variance/mean in the dataset are then included, to provide better iterated mass spectra and improved elution profiles. Finally, all masses are included. The method is shown to work well even when there is no selectivity in the chromatographic direction.