# Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders

arXiv: https://arxiv.org/abs/2403.04526 · HTML: https://mlsanity.com/paper/2403.04526/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 109 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing](https://mlsanity.com/paper/1708.01894/) (2017)
- [Nonlinear unmixing of hyperspectral images: models and algorithms](https://mlsanity.com/paper/1304.1875/) (2013)
- [Deep Hyperspectral Unmixing using Transformer Network](https://mlsanity.com/paper/2203.17076/) (2022)
- [Hyperspectral Unmixing via Deep Autoencoder Networks for a Generalized Linear-Mixture/Nonlinear-Fluctuation Model](https://mlsanity.com/paper/1904.13017/) (2019)
- [High-throughput molecular imaging via deep learning enabled Raman spectroscopy](https://mlsanity.com/paper/2009.13318/) (2020)
- [A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances](https://mlsanity.com/paper/2104.04599/) (2021)

## Built on by (most cited first, 3 of 3)

- [RamanPFN: learning from Raman spectral structure with a tabular foundation model](https://mlsanity.com/paper/2608.02157/) (2026)
- [A Brain-Inspired Deep Separation Network for Single Channel Raman Spectra Unmixing](https://mlsanity.com/paper/2604.22324/) (2026)
- [Bin Latent Transformer (BiLT): A shift-invariant autoencoder for calibration-free spectral unmixing of turbid media](https://mlsanity.com/paper/2605.11829/) (2026)

## Abstract (arXiv)

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.