Laser Electrospray Mass Spectrometry: Mechanistic Insights and Classification of Inorganic-Based Explosives and Tissue Phenotypes Using Multivariate Statistics

Abstract

This dissertation elucidates a greater understanding of the vaporization and electrospray post-ionization mechanisms when using femtosecond laser pulses for desorption of surface molecules and electrospray ionization for capture and mass analysis of the gas phase ions. The internal energy deposition from nonresonant vaporization with femtosecond laser pulses was measured using dried and liquid samples of p-substituted benzylpyridinium ions and peptides. In the comparison of the experiments of using 800 nm and 1042 nm laser pulses, it was found that there are different vaporization mechanisms for dried and liquid samples. It was established that LEMS is a "soft" mass analysis technique as it resulted in comparable internal energy distributions to ESI-MS with one caveat; multiphoton excitation of dried samples results in extensive fragmentation at higher pulse energies. The quantitative aspects of the laser electrospray mass spectrometry (LEMS) technique were established using various multicomponent mixtures of small biomolecules. Experiments with LEMS resulted in similar quantitative characteristics to ESI-MS except that ESI-MS demonstrated a greater degree of ion suppression when using higher concentrations, particularly in the four-component mixture. The lack of ion suppression in the LEMS measurements was due to the ~1% neutral capture efficiency and most likely not a result of nonequilibrium partitioning. This was supported by the excess charge limit not being surpassed in the LEMS experiments and the quantitative analysis requiring the use of response factors. This dissertation also expanded upon the use of multivariate analysis for the classification of samples that were directly mass analyzed without any sample preparation using LEMS. A novel electrospray complexation mixture using cationic pairing agents, a lipid, and sodium acetate enabled the simultaneous detection of positive, neutral and negative charged features of inorganic-based explosive residues in a single experiment. This complexation mixture also enabled the detection of new features from an RDX-based propellant mixture. Principal component analysis (PCA) proved reliable for accurate classifications of the explosive mixtures. PCA was also used for accurate classification of eight phenotypes of Impatiens plant flower petals after mass analysis with LEMS. The PCA loading values were used to identify the key biomarkers in the classification. These important mass spectral features were identified as the biologically-relevant anthocyanins, which are phytochemicals that are responsible for the color of the flower petals.