A Physics-Informed Spectral-Structure Synergy Optimization (SSSO) Method for Consistent and Interpretable Spectral...

Area of Science:

• Chemometrics
• Spectroscopy
• Machine Learning

Background:

• Variable selection is crucial for spectroscopic calibration but often overlooks physicochemical mechanisms.
• Existing data-driven methods lack interpretability and consistency.

Purpose of the Study:

• To propose a physics-informed spectral-structure synergy optimization (SSSO) method.
• To enhance the consistency and interpretability of variable selection in spectroscopic calibration.
• To integrate characteristic spectral lineshapes (CSLs) and structured synergy effects.

Main Methods:

• Physics-informed sparse Bayesian dictionary learning to model CSLs.
• Variational Bayesian inference (VBI) for spectral decomposition into meaningful structures.
• Structure-based bootstrap sampling to select optimal synergistic structural combinations.

Main Results:

• SSSO achieves superior predictive performance compared to existing methods.
• The method ensures physicochemical interpretability by aligning selected variables with chemical bonds.
• Adaptive nonuniform spectral segmentation based on chemically meaningful peak structures.

Conclusions:

• The SSSO method offers a robust framework for physics-informed variable selection in spectroscopy.
• Integrating spectral lineshapes and structural information enhances model interpretability and accuracy.
• This approach advances spectroscopic calibration by bridging data-driven and mechanism-based analyses.