Poster
in
Workshop: AI for Science: Scaling in AI for Scientific Discovery
RamanSPy: Augmenting Raman Spectroscopy Data Analysis with AI
Dimitar Georgiev · Simon Pedersen · Ruoxiao Xie · Álvaro Fernández-Galiana · Molly Stevens · Mauricio Barahona
Keywords: [ Python package ] [ spectral analysis ] [ spectral preprocessing ] [ Artificial intelligence ] [ Raman spectroscopy ] [ chemometrics ] [ Machine Learning ]
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.