Generating chemically relevant image contrast from spectral image data requires multivariate processing algorithms that can categorize spectra according to shape. Conventional chemometric techniques like inverse least squares, classical least squares, multiple linear regression, principle component regression, and multivariate curve resolution are effective for predicting the chemical composition of samples having known constituents, but they are less effective when<i> a priori</i> information about the sample is unavailable. We have developed a multivariate technique called spectral identity mapping (SIM) that reduces the dependence of spectral image analysis on training datasets. The qualitative SIM method provides enhanced spectral shape specificity and improved chemical image contrast. We present SIM results of spectral image data acquired from polymer-coated paper substrates used in the manufacture of pressure sensitive adhesive tapes. In addition, we compare the SIM results to results from spectral angle mapping (SAM) and cosine correlation analysis (CCA), two closely related techniques.
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