A Brain-Inspired Deep Separation Network for Single Channel Raman Spectra Unmixing

2026-04-24

AI Summary

This study addresses the long-standing challenge in Raman spectroscopy of unmixing pure components from a single-channel noisy mixture spectrum containing thousands of candidate substances. Inspired by speech separation, the authors propose RSSNet, a brain-inspired deep separation network that introduces, for the first time, an end-to-end deep unmixing architecture to single-channel Raman spectral analysis, overcoming the noise sensitivity inherent in traditional sparse regression methods. Leveraging a synthetic data training strategy, RSSNet achieves efficient unmixing over large candidate libraries without requiring multispectral inputs. Experimental results demonstrate that the method outperforms existing approaches by more than 4 dB on synthetic data and exhibits strong generalization capability on unseen real-world mixtures of mineral powders.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Web Mining and Content Analysis: Web data integration and cleaning
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Computational social science

Abstract

Raman spectra obtained in real world applications are often a noisy combination of several spectra of various substances in a tested sample. Unmixing such spectra into individual components corresponding to each of the substances is of great value and has been a longstanding challenge in Raman spectroscopy. Existing unmixing methods are predominantly designed to invert an overdetermined mixed model and therefore require multiple mixed spectra as input. However, open domain and/or non-cooperative detection applications in Raman spectroscopy such as controlled substance detection, call for single-channel solutions which can identify individual components from thousands of candidates by analyzing only a single noisy mixed spectrum. To our knowledge, sparse regression is the only existing solution which can cope with this scenario, yet it has very low tolerance to noises and can hardly be applicable in practice. To address these limitations, we introduce a novel neural approach for single-channel Raman spectrum unmixing inspired by speech separation. It aims at solving underdetermined systems and can decompose a noisy mixed spectrum from a library of thousands of components (substances). The core of our method is a deep separation neural network (RSSNet) which takes a mixed spectrum as input and outputs spectra of pure components. We created two synthetic datasets of single-channel Raman spectra unmixing and demonstrated feasibility and superiority of RSSNet on these datasets (outperform competing methods by >4dB). Furthermore, we verified that RSSNet, trained solely on synthetic data, can successfully unmix real-world mixed spectra of mixtures of mineral powders, exhibiting strong generalization. Our approach represents a new paradigm for Raman unmixing and enables new possibilities for fast detection of Raman mixtures.

Problem

• Raman spectra unmixing
• single-channel separation
• underdetermined unmixing
• noisy spectral decomposition
• mixture analysis

Innovation

• Raman spectra unmixing
• single-channel separation
• deep neural network
• underdetermined system
• generalization from synthetic to real data