Investigation of supervised and unsupervised discovery–based chemometric tools to expand the scope of multidimensional gas chromatography

Abstract

Comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a powerful multidisciplinary tool for the separation of complex mixtures since its inception in 1991. When coupled to a time-of-flight mass spectrometer (TOFMS), GC×GC analysis can confidently identify up to thousands of analytes. The wealth of information provided within this three dimensional (3D) data cube is too great for manual scrutinization, hence necessitating non targeted chemometric analysis. Within non-targeted analysis, both supervised and unsupervised algorithms exist, which differ by the presence and absence of sample class labels, respectively. This dissertation describes several studies aimed at critically investigating non-targeted chemometric tools such as tile-based Fisher ratio (F-ratio) analysis and principal component analysis (PCA) for numerous multidimensional GC platforms, including GC×GC-FID, GC×GC TOFMS, and comprehensive 3D GC (GC3)-TOFMS. First, the utility of data binning prior to PCA is demonstrated for a GC×GC-FID dataset of five diesel fuels, wherein the optimum bin size maintains chemical selectivity and improves the signal-to-noise ratio (S/N). Next, the advantage of ranking F-ratio hitlists using the top F-ratio m/z is demonstrated for low concentration comparisons of spiked and un-spiked JP8 jet fuel and the “limit of discovery” is identified as the limit of quantification (LOQ). Tile-based F-ratio analysis is then coupled with an offline one-way analysis of variance (ANOVA) to characterize the geographical differences between five Sicilian wines. An unsupervised algorithm known as tile-based variance rank initiated-unsupervised sample indexing (VRI-USI) is developed to identify sample-to-sample relationships in GC×GC-TOFMS data. Upon application of VRI-USI to a complex multi-fuel dataset, patterns in the resulting k-means clustering index assignments for each hit in the hitlist correctly revealed the presence of classes and sub-classes, which holds promise for future studies requiring chemical characterization of unknown samples. Finally, the first application of PCA to GC3 -TOFMS data is reported herein, wherein the 3D loadings are used to distinguish four jet fuels.