Resolution of GC-MS Data of Complex PAC Mixtures and Regression Modeling of Mutagenicity by PLS

Abstract

The present work describes a strategy to predict the mutagenicity of very complex mixtures of polycyclic aromatic compounds (PAC) from gas chromatography-mass spectrometry (GC-MS) 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 r2 and Q2) 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.