A hyperspectral unmixing framework for energy-loss near-edge structure analysis

Sirong Lu; David J Smith

doi:10.1016/j.ultramic.2020.113096

A hyperspectral unmixing framework for energy-loss near-edge structure analysis

Ultramicroscopy. 2020 Nov:218:113096. doi: 10.1016/j.ultramic.2020.113096. Epub 2020 Aug 24.

Authors

Sirong Lu¹, David J Smith²

Affiliations

¹ School of Engineering for Matter, Transport and Energy, Arizona State University, Tempe, Arizona 85287, USA; Shenzhen Institute for Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China. Electronic address: sironglu@asu.edu.
² Department of Physics, Arizona State University, Tempe, Arizona 85287, USA.

PMID: 32866760
DOI: 10.1016/j.ultramic.2020.113096

Abstract

Extracting different spectral components and their corresponding concentrations from spectrum images is one of the key challenges for electron energy-loss spectroscopy analysis due to the large amount of data, differing spectral features and low signal-to-noise ratio. Here, an open-source software framework of hyperspectral unmixing for energy-loss near-edge fine structure analysis is proposed. This software determines the number of independent spectral components, the signature of each spectral component and the abundance of each spectral component in each pixel, without reference spectrum or prior knowledge of the datasets. This approach should be suitable for automated materials and chemical analysis.

Keywords: EELS; Fine structure; Hyperspectral unmixing.