Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Machine Learning

Background:

• Raman spectroscopy is a key technique for nondestructive, label-free chemical analysis.
• Chemometric signal unmixing is crucial for analyzing mixtures but faces challenges with complexity.
• Existing methods often struggle to accurately identify components and their proportions in intricate samples.

Purpose of the Study:

• To develop and validate novel hyperspectral unmixing algorithms using autoencoder neural networks.
• To enhance the accuracy, robustness, and efficiency of signal unmixing for complex molecular mixtures.
• To demonstrate the applicability of these algorithms in biological settings.

Main Methods:

• Development of autoencoder neural network-based hyperspectral unmixing algorithms.
• Systematic validation using both synthetic and experimental benchmark datasets.
• Application to volumetric Raman imaging data from monocytic cells.

Main Results:

• Autoencoder unmixing algorithms demonstrated superior accuracy and robustness compared to conventional methods.
• Significant improvements in efficiency were observed with the proposed neural network approach.
• Enhanced biochemical characterization of monocytic cells using volumetric Raman imaging data was achieved.

Conclusions:

• Autoencoder neural networks represent a powerful advancement for Raman spectroscopy signal unmixing.
• These methods overcome limitations of traditional chemometrics in complex mixture analysis.
• The approach shows significant potential for applications in biological and chemical characterization.