RamanSPy: Augmenting Raman Spectroscopy Data Analysis with AI

Dimitar Georgiev, Simon Vilms Pedersen, Ruoxiao Xie, Álvaro Fernández-Galiana, Molly M. Stevens, Mauricio Barahona

Keywords: Raman spectroscopy, spectral analysis, chemometrics, spectral preprocessing, artificial intelligence, machine learning, Python package

TL;DR: We developed a modular, open-source Python package that streamlines the development, validation and deployment of AI-based technologies for Raman spectroscopy data analysis.

Abstract: Raman spectroscopy is a non-destructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of life and material sciences. Recently, there has been a marked increase in the adoption of machine learning techniques in Raman spectroscopic analysis. Nonetheless, progress in the area is still impeded by the lack of software, methodological and data standardisation, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce *RamanSPy*, an open-source Python package for Raman spectroscopic data analysis, which supports day-to-day tasks, integrative analyses, the development of methods and protocols, and the integration of advanced data analytics. *RamanSPy* is highly modular, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis and machine learning in Python. *RamanSPy* is hosted at https://github.com/barahona-research-group/RamanSPy, supplemented with extended online documentation, available at https://ramanspy.readthedocs.io, that includes tutorials, example applications, and details about the real-world research applications presented in this paper.

Submission Number: 12