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\begin{document}

\title{SIGMA: Spectral Interpretation using Gaussian Mixtures and Autoencoder}



\author[1]{Po-Yen Tung}%
\author[1]{Hassan Aftab Sheikh}%
\author[1]{Matthew R Ball}%
\author[2]{Farhang Nabiei}%
\author[1]{Richard Harrison}%
\affil[1]{University of Cambridge}%
\affil[2]{MediaTek Research}%


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  \date{June 14, 2024}


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\begin{abstract}
Identification of unknown micro- and nano-sized mineral phases is
commonly achieved by analyzing chemical maps generated from
hyperspectral imaging datasets, particularly scanning electron
microscope - energy dispersive X-ray spectroscopy (SEM-EDS). However,
the accuracy and reliability of mineral identification are often limited
by subjective human interpretation, non-ideal sample preparation, and
the presence of mixed chemical signals generated within the
electron-beam interaction volume. Machine learning has emerged as a
powerful tool to overcome these problems. Here, we propose a
machine-learning approach to identify unknown phases and unmix their
overlapped chemical signals. This approach leverages the guidance of
Gaussian mixture modeling clustering fitted on an informative latent
space of pixel-wise elemental datapoints modeled using a neural network
autoencoder, and unmixes the overlapped chemical signals of phases using
non-negative matrix factorization. We evaluate the reliability and the
accuracy of the new approach using two SEM-EDS datasets: a synthetic
mixture sample and a real-world particulate matter sample. In the
former, the proposed approach successfully identifies all major phases
and extracts background-subtracted single-phase chemical signals. The
unmixed chemical spectra show an average similarity of 83.0\% with the
ground truth spectra. In the second case, the approach demonstrates the
ability to identify potentially magnetic Fe-bearing particles and their
background-subtracted chemical signals. We demonstrate a robust approach
that brings a significant improvement to mineralogical and chemical
analysis in a fully automated manner. The proposed analysis process has
been built into a user-friendly Python code with a graphical user
interface for ease of use by general users.%
\end{abstract}%



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\textbf{Hosted file}

\verb`essoar.10511396.1.docx`  available at \url{https://authorea.com/users/551607/articles/604431-sigma-spectral-interpretation-using-gaussian-mixtures-and-autoencoder}

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