Modular and Automated Workflow for Streamlined Raman Signal Analysis

TL;DR

This paper introduces a modular, automated workflow for preprocessing and analyzing Raman spectra, including noise reduction, baseline correction, spike handling, and peak fitting, demonstrated on synthetic and real data.

Contribution

It presents a comprehensive, open-source pipeline for Raman signal analysis that integrates preprocessing and peak fitting in a streamlined, automated manner.

Findings

Effective noise and baseline correction demonstrated

Accurate peak parameter extraction achieved

Open-source code facilitates reproducibility

Abstract

Raman spectroscopy is a powerful tool for material characterization. However, careful preprocessing is required for the identification and handling of noise, baseline drift, and random spikes. This paper presents a comprehensive approach to generating and preprocessing Raman spectra. Additionally, we describe methods for fitting Voigt peaks to the spectrum to determine peak parameters. The effectiveness of these methods is demonstrated using both synthetic and real Raman spectra, with code provided in an open-source GitHub repository.