Using mean field approach independent component analysis to fatty acid characterization with overlapped GC-MS signals

In this paper, mean field independent component analysis (MF-ICA) was applied as a deconvolution method to separate complex gas chromatographic-mass spectrometric (GC-MS) signals obtained from fatty acid analysis of fish oil. The separation which is a blind operation was used as a complementary method in identification of the unknown components of a mixture and in quantification purposes, as well. In MF-ICA, the sources (mass spectra) are recovered from the mean of their posterior distributions and mixing matrix (chromatograms) and noise level are estimated through the maximum a posterior (MAP) solution. The number of independent components (ICs) in the overlapping signals can be estimated by the difference between the reconstructed and original GC-MS data. It was found that the chromatographic profiles and the mass spectra of the components in overlapping multicomponent GC-MS data can be accurately recovered with and without previously background correction. The resolved mass spectral sources satisfactory are identified using mass spectral search system. The recovered chromatographic area and the relative content of each analyte considering selected number of ICs are calculated and the results are compared with the ones obtained previously by using heuristic evolving latent projections (HELP) method.