Hierarchical cluster analysis (HCA) is extensively used for the analysis of hyperspectral data. In this work, hyperspectral data sets obtained from Raman maps were analyzed using an alternative mode of cluster analysis, clustering “images” instead of spectra, under the assumption that
images showing similar spatial distributions are related to the same chemical species. Such an approach was tested with two Raman maps: one simple “test map” of micro-crystals of four different compounds for a proof of principle and a map of a biological tissue (i.e., cartilage) as an example of chemically complex sample. In both cases, the “image-clustering” approach gave similar results as the traditional HCA, but at lower computational effort. The alternative approach proved to be particularly helpful in cases, as for the cartilage tissue, where concentration gradients of chemical composition are present. Moreover, with this approach, yielded information about correlation between bands in the average spectrum makes band assignment and spectral interpretation easier.
