Machine Learning Model for Multiplexed Surface-Enhanced Raman Scattering Quantification in Skin

M Hashemi; JB King; M Chen; M Relvas; M Aranda; S Abalde Cela; L Dieguez; JW Tunnell

doi:10.1364/TRANSLATIONAL.2022.TW3B.7

Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN)
Technical Digest Series (Optica Publishing Group, 2022),
paper TW3B.7
•https://doi.org/10.1364/TRANSLATIONAL.2022.TW3B.7

Machine Learning Model for Multiplexed Surface-Enhanced Raman Scattering Quantification in Skin

M Hashemi, JB King, M Chen, M Relvas, M Aranda, S Abalde Cela, L Dieguez, and JW Tunnell

Clinical and Translational Biophotonics 2022

Fort Lauderdale, Florida United States
24–27 April 2022
ISBN: 978-1-957171-03-6

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M. Hashemi, J. King, M. Chen, M. Relvas, M. Aranda, S. A. Cela, L. Dieguez, and J. Tunnell, "Machine Learning Model for Multiplexed Surface-Enhanced Raman Scattering Quantification in Skin," in Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN), Technical Digest Series (Optica Publishing Group, 2022), paper TW3B.7.

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Abstract

Implantable SERS devices suffer from light attenuation and tissue background signals, limiting their remote monitoring applications. We demonstrate a novel approach using machine learning and a reference Raman reporter in phantoms and ex vivo skin.

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