Recent advances in nanomaterial-based spectroscopy, particularly Raman and surface-enhanced Raman spectroscopy (SERS), have significantly expanded our ability to probe chemical and biological systems with high sensitivity. However, extracting reliable and reproducible information from spectral data remains a major challenge due to noise, background interference, and variability across experimental conditions.

At the same time, the rapid emergence of artificial intelligence (AI) and machine learning (ML) is transforming how spectral data are analyzed, interpreted, and utilized. Modern spectroscopy is increasingly data-rich, and traditional analysis approaches alone are often insufficient to fully capture complex spectral features, nonlinear relationships, and subtle variations across datasets. AI and ML methods now offer powerful tools for automated feature extraction, classification, quantitative prediction, and even physics-informed modeling of spectral responses, and these developments are a major driving force behind this webinar.

This webinar brings together experts in spectral preprocessing, chemometrics, and machine learning to address these challenges from both methodological and practical perspectives. Topics will include baseline correction and normalization strategies, multivariate data analysis, and emerging AI-driven approaches for spectral interpretation and prediction. In addition, we will introduce the SpectraGuru platform as an example of a reproducible and FAIR-aligned infrastructure designed to support data-driven and AI-enabled spectroscopy research.

We hope this webinar will provide both fundamental insights and practical guidance for researchers working at the intersection of nanomaterials, spectroscopy, and data science, and help advance the integration of AI/ML into next-generation spectroscopic analysis.

Date: 15 September 2026
Time: 3:00pm CEST | 9:00am EDT | 9:00pm CST Asia
Webinar ID: 813 1368 4214
Webinar Secretariat: journal.webinar@mdpi.com