Nanomaterials Webinar Highlights AI-Enabled Spectroscopy and SpectraGuru

On Tuesday, September 15, 2026, the webinar “Spectral Preprocessing, Chemometrics, and Machine Learning for Nanomaterial-Based Spectroscopy” brought together researchers working at the intersection of nanomaterials, Raman/SERS spectroscopy, data science, and artificial intelligence. The online event attracted 128 attendees, reflecting growing interest in reliable spectral data analysis and AI-enabled spectroscopy.

The webinar provided an integrated view of the modern spectral-analysis workflow, spanning spectral preprocessing, chemometrics, machine learning and deep learning, and open-source research infrastructure.

Prof. Kimberly Hamad-Schifferli discussed spectral preprocessing strategies for Raman and SERS analysis, demonstrating how choices in preprocessing can influence spectral features and ultimately affect downstream interpretation. Her presentation emphasized the importance of carefully designed preprocessing workflows for obtaining reliable and reproducible results.

Dr. Xianyan Chen introduced chemometric approaches for extracting meaningful information from complex spectral datasets. She highlighted multivariate statistical and machine-learning methods, including principal component analysis (PCA) and support vector machines (SVM), and demonstrated how these approaches can support pattern recognition, classification, and quantitative spectral analysis.

Dr. Bo Hu presented machine-learning and deep-learning approaches for spectroscopy, illustrating how large spectral datasets can be combined with advanced computational models to improve spectral classification and enable more sophisticated data-driven analysis of chemical and biological systems.

The webinar concluded with a presentation and live demonstration of SpectraGuru, a free and open-source platform for spectral visualization, preprocessing, analytics, and AI-enabled spectroscopy. Fengbo Ma introduced the platform and demonstrated how researchers can perform spectral-data processing and analysis through an integrated and accessible workflow. The presentation also highlighted the open-source nature of SpectraGuru and invited researchers from the broader spectroscopy community to use, test, and contribute to the platform.

Together, the presentations demonstrated how rigorous spectral preprocessing, chemometrics, machine learning, and open research infrastructure can work as a connected ecosystem for next-generation Raman and SERS analysis. The strong participation of 128 attendees also underscored the growing community interest in combining spectroscopy with modern data science and artificial intelligence.

The webinar was organized through Nanomaterials/Sciforum and provided an opportunity for researchers from spectroscopy, nanomaterials, statistics, and AI communities to exchange ideas and explore emerging directions in reproducible, data-driven, and AI-enabled spectroscopy.

To find more information, please go to https://sciforum.net/event/Nanomaterials-30.