Compound identification in gas chromatography/mass spectrometry-based metabolomics by blind source separation.

摘要: Abstract Metabolomics GC–MS samples involve high complexity data that must be effectively resolved to produce chemically meaningful results. Multivariate curve resolution-alternating least squares (MCR-ALS) is the most frequently reported technique for purpose. More recently, independent component analysis (ICA) has been as an alternative MCR. Those algorithms attempt infer a model describing observed and, therefore, regression used in MCR assumes linear combination of model. However, due real data, construction describe optimally critical step and these should prevent influence from outlier data. This study proves (ICR) compound identification. Both ICR though require correctly resolve mixtures. In this paper, novel orthogonal signal deconvolution (OSD) approach introduced, which uses principal determine spectra. The includes identification comparison between results by ICA-OSD, MCR-OSD, MCR-ALS using pure standards human serum samples. Results shows may multivariate methods, efficiency comparable MCR-ALS. Also, demonstrates proposed OSD achieves greater spectral resolution accuracy than traditional when compounds elute under undue interference biological matrices.