Multivariate statistical analysis characterization of application-based ion mobility spectra

Ion mobility spectral datasets were investigated for the potential to discriminate between classes of compounds using multivariate statistical analysis techniques. Entire ion mobility spectra, including the reactant ion peak (RIP), were obtained using a hand-held gas chromatography-ion mobility spectrometer (GC-IMS) to ensure vapor quality through Chromatographic prefractionation. The chosen datasets were application-based and consisted of (1) 15 compounds representative of illegal drug synthesis precursors/purification solvents, (2) 18 compounds that are airborne contaminants in the NASA space shuttle, (3) benzene, toluene, xylenes and six polyaromatic hydrocarbons among 41 alkane, alkene and alkylaromatic compounds typical of petroleum-based environmental contaminants. Principal component and discriminant rotation analyses of these datasets satisfactorily separated the various classes of compounds from each other. All spectra displayed an RIP that was between 20–75% of its maximum, and either the monomer or monomer and dimer peaks were present for every compound in the datasets. Despite these relatively wide ranges in the ion mobility response characteristics, it appears that there is potential for multivariate statistical analysis techniques to discriminate between the ion mobility spectra of a diverse set of compounds.