Label-free surface-enhanced Raman scattering (SERS) and machine learning for biological analysis

Der Vang; Jonathan Pahren; Tom Cambron; Pietro Strobbia

doi:10.1117/12.3013981

6 June 2024 Label-free surface-enhanced Raman scattering (SERS) and machine learning for biological analysis

Der Vang, Jonathan Pahren, Tom Cambron, Pietro Strobbia

Proceedings Volume 13059, Smart Biomedical and Physiological Sensor Technology XXI; 130590C (2024) https://doi.org/10.1117/12.3013981
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Understanding biological samples is an important part of disease treatment and prevention. Current methods of biological analysis can be time-consuming and costly. Label-free Surface-Enhanced Raman Scattering (SERS) is a useful vibrational technique that incorporates plasmonic metal nanomaterial to amplify Raman signals. This technique requires little sample preparation and provides high informational chemical insights on the target. Herein, we use SERS to test and analyze biological samples of exosomes and bacteria. Each biological sample has similar biomolecular components that are difficult to differentiate or show small differences after interacting with other chemicals. Thus, herein, we show the incorporation of principal component analysis to understand differences and trends in the spectra. These studies highlight the powerful combination of SERS and machine learning for biological analysis.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Der Vang, Jonathan Pahren, Tom Cambron, and Pietro Strobbia "Label-free surface-enhanced Raman scattering (SERS) and machine learning for biological analysis", Proc. SPIE 13059, Smart Biomedical and Physiological Sensor Technology XXI, 130590C (6 June 2024); https://doi.org/10.1117/12.3013981

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