INTERACTIVE SELF-MODELING MULTIVARIATE-ANALYSIS OF THERMOLYSIS MASS-SPECTRA

Traditionally, multivariate data analysis has been characterized as cross-reference (i.e. principal components, spectra, loading and score lists) intensive in terms of data reduction, as well as conceptually difficult to understand. The variance diagram (VARDIA) greatly simplified multivariate analysis by shifting the burden from complex lists of data variable statistics to visually informative depictions of the data set. The visually oriented VARDIA technique was combined with the KEY SET algorithm developed by Malinowski, and their union, the Interactive Self-Modeling Multivariate Analysis (ISMA) technique, was shown to be particularly effective for identifying pure component spectra in time-resolved Raman and Fourier transform IR spectral data sets. Furthermore, color labeling of the pure variables and components, which also characterizes the ISMA approach, provided enormous benefits with respect to data analysis and user interaction. In the present study, time-resolved atmospheric pressure ionization thermolysis mass spectra of biopolymers, biopolymer mixtures and microorganisms are investigated by the ISMA approach. A straightforward, user-friendly resolution of the mass spectra was realized by color-graphics displays of the ISMA technique. Color is not only a convenient tool in the data reduction process, but it also acts as a link between the multivariate and original data sets. Separate components could be traced via their color in the VARDIA, pure masses and standard deviation mass spectra.