INTERACTIVE SELF-MODELING MIXTURE ANALYSIS

In the analytical environment, spectral data resulting from the analysis of samples often represent mixtures. To extract information about pure components often is a major problem, especially when reference spectra are not available. For this type of problem, self-modeling mixture analysis techniques have been developed. Although successful commercial applications have been developed, the application of these techniques to complex data sets requires skilled operators. Furthermore, no general purpose software is available. In order to make self-modeling mixture analysis more accessible, a new method has been developed. For the approach described here, all the intermediate steps can be presented straightforwardly in the form of spectra, and it is possible to direct the procedure by using chemical knowledge of the samples. Examples will be shown of Raman spectroscopic data of a reaction, where spectra of intermediates are extracted, and of FT-IR microscopic data of a polymer laminate, where it will be shown that spectra of layers below the resolution of the FT-IR microscope can be calculated.