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

Area of Science:

• Materials Science
• Spectroscopy
• Data Analysis

Background:

• Electron energy-loss spectroscopy (EELS) analysis faces challenges in extracting spectral components and concentrations from complex spectrum images.
• Large data volumes, diverse spectral features, and low signal-to-noise ratios hinder accurate analysis.

Purpose of the Study:

• To develop an open-source software framework for hyperspectral unmixing in EELS analysis.
• To enable automated determination of spectral components and their abundances without prior knowledge.

Main Methods:

• Implementation of a hyperspectral unmixing algorithm within an open-source software framework.
• The software identifies the number of independent spectral components, their signatures, and their abundance in each pixel.

Main Results:

• The proposed software successfully extracts spectral components and concentrations from spectrum images.
• It operates without requiring reference spectra or prior dataset knowledge.

Conclusions:

• The developed software provides a robust solution for analyzing EELS data.
• This approach facilitates automated materials and chemical analysis through accurate spectral component extraction.