APPLICATION OF COMPUTER PATTERN RECOGNITION TO METAL ION CHEMICAL IONIZATION MASS SPECTROMETRY DATA (FTMS, MULTIVARIATE ANALYSIS)
Abstract
The first application of computer pattern recognition to the analysis of low pressure transition metal ion chemical ionization (MICI) data is described. The data have been collected using a conventional ion cyclotron resonance (ICR) mass spectrometer and a Fourier transform mass spectrometer (FTMS) equipped with laser ionization sources. Chemical ionization data for organic compounds of several classes with various transition metal ions are analyzed using a pattern recognition system written in our laboratory for the IBM 9000 lab computer in IBM version 9002 FORTRAN 77. Feature selection routines employing the K-nearest neighbor (KNN) algorithm with a novel weighting of features are used to select sets of mass peaks which best separate the chemical classes. The data are analyzed with two major objectives: first, the analytical utility of MICI is quantitated using the best recognition accuracies of organic classes as a measure of effectiveness, and second, the trends in reactivity of the various metal ions are examined by pattern recognition methods.
Degree
Ph.D.
Subject Area
Analytical chemistry
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