Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders
Author(s)
Type
Journal Article
Abstract
Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples
in a non-destructive, label-free manner. Many applications entail the unmixing of signals from mixtures of
molecular species to identify the individual components present and their proportions, yet conventional methods
for chemometrics often struggle with complex mixture scenarios encountered in practice. Here, we develop
hyperspectral unmixing algorithms based on autoencoder neural networks, and we systematically validate them
using both synthetic and experimental benchmark datasets created in-house. Our results demonstrate that
unmixing autoencoders provide improved accuracy, robustness and efficiency compared to standard unmixing
methods. We also showcase the applicability of autoencoders to complex biological settings by showing improved
biochemical characterization of volumetric Raman imaging data from a monocytic cell.
in a non-destructive, label-free manner. Many applications entail the unmixing of signals from mixtures of
molecular species to identify the individual components present and their proportions, yet conventional methods
for chemometrics often struggle with complex mixture scenarios encountered in practice. Here, we develop
hyperspectral unmixing algorithms based on autoencoder neural networks, and we systematically validate them
using both synthetic and experimental benchmark datasets created in-house. Our results demonstrate that
unmixing autoencoders provide improved accuracy, robustness and efficiency compared to standard unmixing
methods. We also showcase the applicability of autoencoders to complex biological settings by showing improved
biochemical characterization of volumetric Raman imaging data from a monocytic cell.
Date Issued
2024-11-05
Date Acceptance
2024-09-10
Citation
Proceedings of the National Academy of Sciences of USA, 2024, 121 (45)
ISSN
0027-8424
Publisher
National Academy of Sciences
Journal / Book Title
Proceedings of the National Academy of Sciences of USA
Volume
121
Issue
45
Copyright Statement
© 2024 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
License URL
Identifier
https://www.pnas.org/doi/10.1073/pnas.2407439121
Publication Status
Published
Article Number
e2407439121
Date Publish Online
2024-10-29