Abstract

In the analytical environment, spectral data resulting from analysis of samples often represent mixtures of several components. Extraction of information about pure components from that kind of mixture is a major problem, especially when reference spectra are not available. Self-modeling multivariate mixture analysis has been developed for this type of problem. In this paper, two examples will be used to show the potential of the technique for vibrational spectroscopy. Infrared microspectroscopic chemical imaging has been employed to improve spatial resolution for distinguishing differences between adjacent, nonidentical materials. The resolution of a 2- to 3-μm-thick inner layer, from a four-layer polymer laminate, has been achieved. The same approach has been utilized to extract pure component spectra out of a KBr pellet of a mixture of three compounds.

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