Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders

arXiv:2403.04526v1 Announce Type: new
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 …

abstract applications arxiv autoencoders components cs.ai cs.cv cs.lg domains free identify physics samples spectroscopy type via