Most image-based analyses, using absorbance or fluorescence of the spatial distribution of identifiable structures in complex biological systems, use only a very small number of dimensions of possible spectral data for the generation and interpretation of the image. We here extend the concepts of hyperspectral imaging, being developed in remote sensing, into analytical biotechnology. The massive volume of information contained in hyperspectral spectroscopic images requires multivariate analysis in order to extract the chemical and spatial information contained within the data. We here describe the use of multivariate statistical methods to map and quantify common protein staining fluorophores (SYPRO Red, Orange and Tangerine) in electrophoretic gels. Specifically, we find (a) that the 'background' underpinning limits of detection is due more to proteins that have not migrated properly than to impurities or to ineffective destaining, (b) the detailed mechanisms of staining of SYPRO red and orange are apparently not identical, and in particular (c) that these methods can provide two orders of magnitude improvement in the detection limit per pixel, to levels well below the limit observable optically.