Transfer-Learning Deep Raman Models Using Semiempirical Quantum Chemistry.

Area of Science:

• Biophotonics
• Spectroscopy
• Computational Chemistry

Background:

• Raman spectroscopy provides specific molecular information with minimal sample preparation.
• Its application is widespread, often enhanced by chemometrics, machine learning (ML), and deep learning (DL).
• A key challenge is the lack of large, independent Raman spectra databases, hindering model training and generalizability.

Purpose of the Study:

• To address the data scarcity issue in Raman spectroscopy for deep learning model training.
• To develop a scalable framework for spectral analysis using synthetic data.
• To improve the generalizability and reduce the computational cost of deep Raman models.

Main Methods:

• Generating simulated vibrational spectra using semiempirical quantum chemistry methods.
• Pretraining deep learning models on large synthetic spectral datasets.
• Fine-tuning pretrained models on smaller, experimental bacterial Raman spectra datasets.
• Employing transfer learning techniques.

Main Results:

• Synthetic data enabled efficient pretraining of deep learning models.
• Transfer learning achieved performance comparable to models trained from scratch.
• The approach significantly reduced computational costs.
• Validated the utility of synthetic data for deep Raman model development.

Conclusions:

• Synthetic data generation is a viable strategy for pretraining deep Raman models.
• Transfer learning offers a scalable and cost-effective framework for spectral analysis.
• This method is particularly beneficial for resource-limited settings in biophotonics.