Gas chromatography is a powerful separation technique that alone, and when coupled with mass spectrometric detection, can provide detailed information regarding the chemical composition of complex mixtures. Advanced chemometric algorithms are often applied to the data generated from these gas chromatographic separations in order to glean additional meaningful information from large and complex data sets. This dissertation presents several research investigations conducted on the development, optimization, application and study of several chemometric algorithms applied to one- and two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (TOFMS). The two-dimensional mass cluster method and principal component analysis (PCA) were applied to a non-targeted investigation of the stable-isotope incorporation of metabolites present in the metabolome of the methylotrophic bacteria Methylobacterium extorquens AM1 using gas chromatography time-of-flight mass spectrometry (GC-TOFMS). The area under the curve (AUC) of receiver operating characteristic (ROC) curves were used as quantitative metrics for the optimization of the tile-based Fisher ratio method using diesel fuel spiked with native and non-native analytes using comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC – TOFMS). This optimized algorithm was then applied to a process analytical chemistry (PAC) investigation into the source of catalyst yield reduction in an industrial polymerization plant. Finally, a GC-TOFMS simulation-based study determined the chemometric limit of resolution for deconvoluting analytes using multivariate curve resolution alternating least squares (MCR-ALS) and compared the results to expected theory surrounding the probability of peak overlap.