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
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.