Prof. Alexander Khmaladze
University at Albany, USA

Title: Raman Spectral Analysis of Complex Biological Systems with Machine Learning

Abstract: Raman micro-spectroscopy is widely used for chemical composition mapping within live biological samples, such as cells, organoids, and tissues. It permits non-invasive and non-destructive measurements that do not require special sample preparation processes, such as dye labelling or staining. While conventional spectral analysis techniques have been employed to extract useful patterns from Raman data, emerging developments in machine learning offer new opportunities to advance the field. In this presentation, I will discuss the applications of Raman spectroscopy to brain tissue sections, tissue engineering samples and cells exposed to iron. Through examination of spectral features in the Raman data, we detected distinct molecular bond signatures indicative of changes in key biomolecules, including proteins, lipids, and nucleic acids. We applied supervised machine learning models (Random Forest, Support Vector Machine), as well as Singular Value Decomposition to classify Raman spectra and capture patterns in the spectral data, enabling accurate differentiation between treated and control groups.

BIO: Dr. Alexander Khmaladze is an Associate Professor in the Physics Department at State University of New York (SUNY) at Albany. He received his Ph.D. from the University of South Florida, where he published a number of papers on digital holographic phase imaging. He then accepted a postdoctoral position at the University of Michigan, where he worked on the application of near-infrared Raman Spectroscopy to monitoring of tissue constructs implanted in mice, with the ultimate goal of applying this technique to human patients. Dr. Khmaladze joined the Physics Department of SUNY at Albany in September 2014. Currently, his lab has several digital holographic microscopic setups, 3D Cell imaging tomographic microscope, and a portable Raman microscopic system. His research interests include Raman spectroscopy and microscopy, three-dimensional digital holographic imaging, microscope design, hyperspectral imaging of live cells and biological tissue imaging.