Resolution of GC-MS data of complex PAC mixtures and regression modeling of mutagenicity by PLS

The present work describes a strategy to predict the mutagenicity of very complex mixtures of polycyclic a romatic compounds (PAC) from gas chromatography-mass spectrometry [GC-MSI patterns of the mixtures, each containing 260 compounds on,average. The mixtures, 13 organic extracts of exhaust particles, were characterized by full scan GC-MS. The data were resolved into peaks and spectra for individual compounds by an automated curve resolution Procedure. Similarity between spectra was evaluated for peaks that appeared within a time interval of 4 min, using a similarity index of 0.8 to ascertain that the same compound was represented: by the same variable name (retention time) in all samples. The resolved chromatograms were integrated, resulting in a predictor matrix of size 13 x 721, which was used as input to a multivariate regression model. Partial least-squares projections to latent structures (PLS) were used to correlate the GC-MS chromatograms to mutagenicity as measured in the Ames Salmonella assay. The best model (high r(2) and Q(2)) was obtained with 52 variables. These variables covary with: the observed mutagenicity, and may subsequently be identified chemically. Furthermore, the regression model can be used to predict mutagenicity from GC-MS chromatograms of other organic extracts.