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Mid-infrared (IR), Raman, and X-ray fluorescence (XRF) spectroscopy methods, as well as mass spectrometry (MS), can be used for 3D chemical imaging. These techniques offer an invaluable opportunity to access chemical features of biological samples in a nonsupervised way. The global chemical information they provide enables the exploitation of a large array of chemical species or parameters, so-called ‘spectromics’. Extracting chemical data from spectra is critical for the high-quality chemical analysis of biosamples. Furthermore, these are the only currently available techniques that can quantitatively analyze tissue content (e.g., molecular concentrations) and substructures (e.g., cells or blood vessels). The development of chemical-derived biological metadata appears to be a new way to exploit spectral information with machine learning algorithms. [ABSTRACT FROM AUTHOR]