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 statistic al analysis techniques. Entire ion mobility spectra, including the rea ctant ion peak (RIP), were obtained using a hand-held gas chromatograp hy-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 dru g synthesis precursors/purification solvents, (2) 18 compounds that ar e airborne contaminants in the NASA space shuttle, (3) benzene, toluen e, xylenes and six polyaromatic hydrocarbons among 41 alkane, alkene a nd alkylaromatic compounds typical of petroleum-based environmental co ntaminants. Principal component and discriminant rotation analyses of these datasets satisfactorily separated the various classes of compoun ds from each other. AU spectra displayed an RIP that was between 20-75 % of its maximum, and either the monomer or monomer and dimer peaks we re present for every compound in the datasets. Despite these relativel y wide ranges in the ion mobility response characteristics, it appears that there is potential for multivariate statistical analysis techniq ues to discriminate between the ion mobility spectra of a diverse set of compounds.