Compound identification in comprehensive gas chromatography—mass spectrometry-based metabolomics by blind source separation

Abstract

Comprehensive gas chromatography - mass spectromety (GCxGC-MS) has become a promising tool in metabolomics. However, algorithms for GCxGCMS data processing are needed in order to automatically process the data and extract the most pure information about the compounds appearing in the complex biological samples. This study shows the capability of orthogonal signal deconvolution (OSD), a novel algorithm based on blind source separation, to extract the spectra of the compounds appearing in GCxGC-MS samples. Results include a comparison between OSD and multivariate curve resolution - alternating least squares (MCRALS) with the extraction of metabolites spectra in a human serum sample analyzed through GCxGC-MS. This study concludes that OSD is a promising alternative for GCxGC-MS data processing.