A pre-training enhanced deep learning framework for robust sparse unmixing in chemical imaging.

Area of Science:

• Advanced chemical imaging analysis
• Spectroscopic data interpretation
• Machine learning applications in chemistry

Background:

• Chemical imaging techniques like hyperspectral and Raman imaging offer non-destructive analysis of complex mixtures.
• Accurate decomposition of mixed pixel spectra is challenging due to spectral overlaps and variability.
• Deep learning models for unmixing can be unstable and sensitive to initialization.

Purpose of the Study:

• To develop a versatile pre-training framework (P4SU) to enhance sparse unmixing for chemical imaging.
• To improve the accuracy and stability of deep learning models in spectral unmixing.
• To provide a robust solution for analyzing complex chemical mixtures.

Main Methods:

• P4SU utilizes simulated spectra from spectral libraries for deep learning model pre-training.
• The framework incorporates both linear and nonlinear decoder options for diverse mixing scenarios.
• Models are fine-tuned on target chemical imaging data for optimal performance.

Main Results:

• P4SU demonstrated superior accuracy and stability across pigment, sugar solution, and pharmaceutical tablet datasets.
• Pre-trained models reduced root mean square error (RMSE) by 15-32% compared to non-pre-trained models.
• P4SU significantly lowered the standard deviation of results by 90-98% on pigment mixtures.

Conclusions:

• P4SU shows robustness to spectral variability and noise, enabling rapid and reliable chemical analysis.
• The method is applicable for quality control and material identification in chemical imaging.
• An open-source Python toolkit is available, streamlining analytical workflows.