A primary study on resolution of overlapping GC-MS signal using mean-field approach independent component analysis

Independent component analysis (ICA) has been found to be powerful to separate complex signals. However, chemical signals are generally correlated, instead of independent as hypothesized in ICA. In this study, mean-field independent component analysis (MF-ICA) was investigated to resolve the overlapping gas chromatographic-mass spectrometric (GC-MS) signal. In MF-ICA, the sources are estimated from the mean of their posterior distribution. The mixing matrix and noise level are found through the maximum a posterior (MAP) solution. By simulated signals, results show that for cases of the slightly correlated (or overlapped) sources, both the sources (MS) and mixing matrix (chromatogram) can be almost correctly estimated by specification of the nonnegative (positive) priors for the mixing matrix and sources. However, when the sources are highly correlated, no good results can be obtained, although acceptable estimated sources can be obtained somehow for database matching. For experimental overlapping GC-MS data, reasonable results are obtained, because MS spectra of different homologous compounds in GC-MS analysis of a mixture are not generally correlated very much. Therefore, ICA should be an alternate tool for resolution of overlapping chemical signals, although further works are still needed.