Multicomponent Raman spectral regression using complete and incomplete models and convolutional neural networks.

Area of Science:

• Spectroscopy
• Chemometrics
• Machine Learning

Background:

• Hyperspectral Raman imaging generates large datasets, posing analytical challenges.
• Conventional preprocessing and regression methods are time-consuming and operator-dependent.
• Existing methods struggle with incomplete spectral data (underdetermined models).

Purpose of the Study:

• To develop a rapid and automated solution for analyzing hyperspectral Raman imaging data.
• To address the bottleneck in processing large spectral datasets.
• To create a model robust to missing spectral information.

Main Methods:

• A convolutional neural network (CNN) was trained exclusively on synthetic data.
• The CNN model integrates background correction and regression into a single step.
• The model was applied to experimental measurements, including underdetermined cases.

Main Results:

• The CNN model demonstrated rapid processing speeds and reduced sensitivity to parameter selection compared to traditional methods like asymmetric least squares (AsLS).
• Performance was comparable to or better than established least squares approaches.
• Validation was successful on both synthetic spectral mixtures and experimental liposome data.

Conclusions:

• The proposed CNN model offers an efficient and automated solution for hyperspectral Raman imaging analysis.
• It effectively handles large datasets and incomplete spectral information without user intervention.
• This AI-driven approach significantly improves upon traditional analytical methods in speed and robustness.