This study demonstrates the use of standard morphological image processing techniques to reduce the hyperspectral image data of samples, containing discrete particles or domains, to a single average spectrum per particle. The processing is automated and successful even when the particles are in contact. Focal Plane Array, Fourier transform infrared (FTIR) absorbance images of biological cells are used as an example dataset. The large number of spectra in the image (∼40 000) can be intelligently averaged to ∼100 mean spectra, approximately one per cell, greatly simplifying further analysis. As well as reducing the data, the morphological analysis provides useful information, such as the size of each cell, and allows every spectrum associated with each cell to be identified and analysed independently of the full dataset. Using these methods, combined with principal components analysis, consistent spectral differences are found between the spectra of the whole cells and a cell region approximately corresponding to the nucleus. These spectral differences compare well with previous IR measurements on whole CALU-1 cells and their isolated nuclei, but with a simpler sample preparation. The algorithm created to analyse the CALU-1 cells has been applied to a second cell line (NL20), which has a very different growth morphology, to demonstrate that this processing method is applicable to varied samples with little or no modification.
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