Microscopic Raman Line-Imaging with Principal Component Analysis

Abstract

Reconstructed Raman images of the distribution of polystyrene and polyethylene were obtained by line-imaging, with the use of univariate and multivariate processing of the spectral data. Multiple sets of microscopic Raman spectral line-images were acquired with line-focused illumination, a motorized translation stage to move the sample perpendicular to the illumination line, and a holographic imaging spectrograph equipped with a 2D detector. The line-imaged raw spectral data were processed with the use of both a simple univariate method (single-band integration) and a more sophisticated multivariate method (principal component analysis with eigenvector rotation) to generate two-dimensional Raman images representing spatial distribution of the polymers. In the spectral range employed (900–1700 cm−1), suitable bands could be found for univariate processing of the polystyrene and polyethylene images. The principal component analysis method gave equivalent separation of the images, but only if the entire spectral window was employed to generate the eigenvectors.