摘要： A three-dimensional (3D) characterization of the morphology of nanostructures can nowadays routinely be obtained using electron tomography. Nevertheless, resolving the chemical composition of complex nanostructures in 3D remains challenging and the number of studies in which electron energy loss spectroscopy (EELS) is combined with tomography is limited. In most of these studies, two dimensional (2D) elemental maps of the object are first extracted at each tilt angle and used as an input for tomographic reconstruction. An alternative approach is to reconstruct each energy loss separately yielding a 4D data cube where an EELS spectrum can be extracted from each 3D voxel. During the last decade, dedicated reconstruction algorithms have been developed for HAADF-STEM tomography which use prior knowledge about the investigated sample. For example, the discrete algebraic reconstruction technique (DART) is based on the idea that a 3D HAADF-STEM reconstruction of a (nano)material only contains a limited number of grey values. In this manner, several artefacts, typical to electron tomography, are mininized leading to reconstructions with a higher reliability. An additional advantage of discrete tomography is that the quantification of the final reconstruction is straightforward since the segmentation is part of the reconstruction algorithm. Here, we will extend discrete tomography to its application for spectroscopic datasets where it is assumed that the experimental spectrum of each reconstructed voxel is a linear combination of a well-known set of references spectra.