Data-Driven Diagnostics: Biomolecular Insights with Advanced Raman Spectroscopy and Machine Learning

Raman spectroscopy is a powerful, label-free tool for the molecular characterization of biological samples, offering insights into cellular states and disease progression. However, complex biological matrices and subtle spectral changes pose challenges to robust and automated analysis. To unlock Raman's diagnostic potential, advanced data science and machine learning (ML) methodologies are essential. This talk presents a comprehensive framework for data-driven diagnostics using Raman spectroscopy. It details effective spectral pre-processing and dimensionality reduction to decode complex spectral fingerprints.

Attendees will explore the application of various machine learning algorithms for classifying biological states, such as differentiating between healthy and diseased tissues or identifying microbial pathogens. The presentation will demonstrate how data-driven approaches significantly enhance diagnostic accuracy, enable the discovery of novel spectral biomarkers, and facilitate the development of rapid, non-invasive diagnostic tools, thereby advancing the field of precision medicine.

About the presenter
Matthew Berry, Ph.D., is an applications scientist at Edinburgh Instruments, specializing in data-driven Raman spectroscopy and photoluminescence for various fields. He holds a bachelor’s degree in chemistry from the University of Glasgow and a doctorate in chemistry jointly awarded by the University of Strathclyde and the University of Edinburgh.

His research has focused on surface-enhanced Raman scattering (SERS) and surface-enhanced spatially offset Raman spectroscopy (SESORS) for biomedical applications, particularly in the detection of bacteria and cancer. Berry has authored multiple peer-reviewed publications in this area and is passionate about bridging cutting-edge spectroscopy with practical applications in healthcare and life sciences.