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.
Kodak European Research Laboratories, Z.I.Nord 71102 Chalon/Saône Cedex, France 2:
Eastman Kodak Company, Rochester, New York 14652-3712, U.S.A.
Publication date: March 1, 1994
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The Society publishes the internationally recognized, peer reviewed journal, Applied Spectroscopy, which is available both in print and online. Subscriptions are included with membership or can be purchased by institutional or corporate organizations. Abstracts may be viewed free of charge. Previously published as Bulletin (Society for Applied Spectroscopy)