Artificial Intelligence-Powered Raman Spectroscopy through Open Science and FAIR Principles

Area of Science:

• Analytical Chemistry
• Materials Science
• Biomedicine

Background:

• Raman spectroscopy is a rapidly advancing analytical technique with broad applications.
• Growth is fueled by instrumentation, chemometrics, and artificial intelligence (AI).
• Challenges in data acquisition, processing, interpretation, and sharing limit its full potential.

Purpose of the Study:

• To review the role of digitalization, Open Science, and FAIR data in Raman spectroscopy.
• To explore digital tools, open resources, and AI applications in the field.
• To propose a roadmap for an open and FAIR ecosystem for Raman spectroscopy.

Main Methods:

• Review of current digital tools and open resources in Raman spectroscopy.
• Assessment of trends in hardware, software, and AI applications.
• Analysis of challenges in data quality, model interpretability, and interoperability.

Main Results:

• The field of Raman spectroscopy is fragmented, with isolated initiatives hindering progress.
• AI shows promise for data collection, analysis, and predictive modeling but faces challenges.
• Standardized data formats, metadata, and ontologies are critical for interoperability and collaboration.

Conclusions:

• A concerted effort toward digitalization and FAIR data principles is essential for robust Raman spectroscopy workflows.
• Addressing fragmentation and promoting standardized, open resources will accelerate progress.
• Developing a sustainable, collaborative ecosystem is key to unlocking the full potential of Raman spectroscopy with AI.