Application of pattern recognition to metal ion chemical ionization mass spectra

TLDR: In this paper, pattern recognition methods were used to evaluate the information content of mass spectrometry data obtained using transition metal ions as an ionization source, and important chemical information was extracted from the raw data by empirical feature selection methods.

Abstract: : Pattern recognition methods were used to evaluate the information content of mass spectrometry data obtained using transition metal ions as an ionization source. Data sets consisting of the chemical ionization mass spectra for Fe+ and Y+ with 72 organics (representing the six classes alkane, alkene, ketone, aldehyde, ether, and alcohol) and 24 alkanes (representing the three subclasses linear, branched, and cyclic) were subjected to pattern recognition analysis using a k-nearest neighbor approach with feature weightings. The reactivities of Fe+ and Y+ toward the classes of compounds studied were characterized using classification accuracies as a measure of selectivity, and important chemical information was extracted from the raw data by empirical feature selection methods. A total recognition accuracy of 81% was obtained for the recognition of the six organic classes and 96% accuracy was obtained for the recognition of the three subclasses of alkanes. Keywords: Artificial intelligence; and Chemical ionization mass spectrometry.