Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders

Dimitar Georgiev, Álvaro Fernández-Galiana, Simon Vilms Pedersen, Georgios Papadopoulos, Ruoxiao Xie, Molly M. Stevens, Mauricio Barahona

Keywords: Raman spectroscopy, hyperspectral analysis, machine learning, autoencoders, chemometrics

TL;DR: We present a framework for Raman spectroscopy chemometrics based on hyperspectral unmixing autoencoder neural networks, achieving more precise chemical analysis than standard methods.

Confirmation Of Submission Requirements: I submit a previously published paper. It was published in an archival peer–reviewed venue on or after September 8th 2024, I specify the DOI in the field below, and I submit the camera-ready version of the paper.

DOI: https://doi.org/10.1073/pnas.2407439121

Submission Number: 24