A Comparative Analysis of Data Synthesis Techniques to Improve Classification Accuracy of Raman Spectroscopy Data.

Area of Science:

• Spectroscopy
• Machine Learning
• Data Science

Background:

• Raman spectra present high-dimensional data challenges, particularly with limited sample sizes for deep learning applications in chemical analysis.
• Acquiring and curating extensive spectral datasets is resource-intensive and requires specialized expertise.
• Deep generative models offer a solution by approximating data distributions to generate synthetic samples.

Purpose of the Study:

• To compare a statistical data synthesis method (weighted blending) with a deep generative model (Variational Autoencoder) for Raman spectra.
• To evaluate the impact of augmenting training data with synthetic spectra on deep learning model performance.
• To assess the robustness and generalization capabilities of models trained with synthetic data.

Main Methods:

• Two binary Raman spectral datasets were used, simulating small sample sizes via 3-fold cross-validation.
• Synthetic data distributions were generated using weighted blending and Variational Autoencoders for each fold.
• Synthetic data were incrementally added to train Convolutional Neural Networks and Fully-Connected Neural Networks.
• Principal Component Analysis and discrete Fréchet distance were used for distribution comparison; balanced accuracy evaluated model performance.

Main Results:

• The study observed trends in model learning as synthetic data were progressively augmented.
• Both weighted blending and Variational Autoencoders generated synthetic distributions.
• Performance metrics like balanced accuracy indicated the impact of synthetic data augmentation on model generalization.

Conclusions:

• Deep generative models, specifically Variational Autoencoders, show potential for generating realistic synthetic Raman spectra.
• Synthetic data augmentation can improve the performance and robustness of deep learning models in spectral analysis tasks.
• The choice of data synthesis method impacts the effectiveness of augmenting limited spectral datasets for machine learning.