Raman imaging using principal components analysis.

Abstract

Raman images of the distribution of materials in mixed, polymeric samples were reconstructed from sets of line-scanned images, using univariate and multivariate processing of the spectral data. Multiple sets of microscopic Raman spectral line-images were acquired 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 2-D CCD detector. The raw spectral data was processed using both a simple univariate method (single band integration) and a more sophisticated multivariate method (principal components analysis with eigenvector rotation) to generate 2-D Raman images representing spatial distribution of the individual polymeric constituents. Proper removal of background signals, such as luminescence, was critical to obtaining good, meaningful results. Indeed, preliminary measurements of background signals suggested that higher magnification objectives favor the signal-to-background ratio, and the spatial filtering provided by the spectrograph entrance slit offered further improvement. The effects of experimental and processing parameters on the principal components analysis (PCA) were also studied. The results indicated that experimental parameters 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.