Raman images of the distribution of materials in a sample prepared from 10-μm-diameter polystyrene spheres embedded in epoxy were reconstructed from sets of line-scanned images, with the use of univariate and multivariate processing of the spectral data. Multiple sets of microscopic Raman spectral line images were acquired by using line-focused illumination with a cylindrical lens, a motorized translation stage to move the sample perpendicular to the illumination line, and a holographic imaging spectrograph equipped with a 2D charge-coupled device (CCD) detector. Repeat sets of data were obtained at different spectrometer slit width settings and different magnification. The raw spectral data were processed by using both a simple univariate method (single-band integration) and a more sophisticated multivariate method [principal components analysis (PCA) with eigenvector rotation] to generate 2D Raman images representing spatial distribution of the individual polymeric constituents. The repeat data sets were compared to ascertain the effects of the sampling parameters on the PCA method. The results indicated that spectrometer slit width and magnification affect the sampling depth and spatial resolution, but have little effect on the PCA. Moreover, digital sampling (i.e., number of PCA wavelength variables) could be significantly reduced with little or no degradation of the PCA-generated images, particularly if key bands were represented.

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