Machine learning driven high-resolution Raman spectral generation for accurate molecular feature recognition

TL;DR

This paper introduces a GAN-based approach to enhance low-resolution portable Raman spectra, enabling high-resolution data generation, noise reduction, and accurate molecular classification for practical, real-time applications.

Contribution

The study presents a novel integration of GANs with portable Raman spectroscopy to generate high-resolution spectra and improve molecular identification accuracy.

Findings

GAN significantly reduces spectral noise.

High-resolution spectra improve molecule classification.

Enhanced robustness over traditional noise removal methods.

Abstract

Through the probing of light-matter interactions, Raman spectroscopy provides invaluable insights into the composition, structure, and dynamics of materials, and obtaining such data from portable and cheap instruments is of immense practical relevance. Here, we propose the integration of a Generative Adversarial Network (GAN) with low-resolution Raman spectroscopy with a portable hand-held spectrometer to facilitate concurrent spectral analysis and compound classification. Portable spectrometers generally have a lower resolution, and the Raman signal is usually buried under the background noise. The GAN-based model could not only generate high-resolution data but also reduced the spectral noise significantly. The generated data was used further to train an Artificial Neural Network (ANN)-based model for the classification of organic and pharmaceutical drug molecules. The high-resolution generated Raman data was subsequently used for spectral barcoding for identification of the pharmaceutical drugs. GAN also demonstrated enhanced robustness in extracting weak signals compared to conventional noise removal methods. This integrated system holds the potential for achieving accurate and real-time monitoring of noisy inputs to obtain high throughput output, thereby opening new avenues for applications in different domains. This synergy between spectroscopy and machine learning (ML) facilitates improved data processing, noise reduction, and feature extraction and opens avenues for predictive modeling and automated decision-making using cost-effective portable devices.