Detection and use of selective ions for providing initial estimates of the concentration vectors in iterative target transformation factor analysis

Abstract

The characteristic of discreteness in mass spectral direction distinguishes GC/MS from other spectral techniques. Based on this feature, we propose a new method to construct the initial concentration vectors for iterative target transformation factor analysis (ITTFA). For each chemical component, a search for a selective ion with good signal-to-noise ratio is first conducted using evolving factor analysis (EFA) and information entropy. The corresponding chromatogram of the selective ion is subsequently applied to construct an initial concentration vector for ITTFA. Special strategies are developed to cope with chromatographic patterns with embedded peaks and complex multicomponent structure. Results from three simulated and one real mixture and comparison with results from heuristic evolving latent projections (HELP) and a previously published method for definition of the initial profiles for ITTFA, indicate that selective ion chromatogram (SIC) ITTFA represents a fast, automatic and accurate method for resolution of GC/MS data.