A0232
Title: Assessment of Raman hyperspectral data from proteins
Authors: Alexander Khmaladze - SUNY at Albany (United States) [presenting]
Abstract: Raman spectroscopy offers a non-destructive, label-free approach to classifying biological samples. The Raman effect is a natural phenomenon of inelastic light scattering determined by the vibrational energy levels of specific molecular structures. There are numerous variations on traditional Raman approaches; many require samples to be labelled with a Raman-sensitive compound. However, for monitoring organic samples, unlabeled techniques are ideal. The interpretation of the Raman spectra of biological samples is often dependent on the spectral resolution of the method, with numerous peaks assigned to biological components like DNA, RNA, protein, or lipids based on prior measurements. Often the Raman signature or spectral trends, rather than individual peaks, are utilized to distinguish between biological conditions. It is demonstrated that Raman signature quantitative comparisons based on multivariate analysis and machine learning can be used to detect structural differences in tissues or discriminate healthy tissue regions from disease- or tumour-burdened regions.
