A direct comparison of univariate and multivariate data analysis has been performed to show the effect of spectral noise on the quality of chemical images derived from hyper-spectral data cubes. A data processing approach has been developed using a numerical model, based on spectra of common pharmaceutical excipients, and then applied to a real multi-layered solid dosage formulation. The results of this study demonstrate that the multivariate analysis, which in its simplest form only de-noises data using principal component analysis (PCA), produces significantly better quality chemical images than the univariate approach, even from data sets which appear visually poor. If pure component spectra are available, ordinary least squares (OLS) regression offers even better results. The ability to de-noise spectra using these approaches impacts on Raman experimental conditions and increases information content collected per unit time. Data acquisition time, which is a rate limiting step in the production of chemical images using Raman mapping and imaging techniques, is reduced by 60% and still produces multivariate chemical images of appropriate quality with which to study pharmaceutical formulations.