Share Email Print

Journal of Biomedical Optics

Raman spectroscopy: <italic<in vivo</italic< quick response code of skin physiological status
Author(s): Raoul Vyumvuhore; Ali M. Tfayli; Olivier Piot; Maud Le Guillou; Nathalie Guichard; Michel Manfait; Arlette Baillet-Guffroy
Format Member Price Non-Member Price
PDF $20.00 $25.00

Paper Abstract

Dermatologists need to combine different clinically relevant characteristics for a better understanding of skin health. These characteristics are usually measured by different techniques, and some of them are highly time consuming. Therefore, a predicting model based on Raman spectroscopy and partial least square (PLS) regression was developed as a rapid multiparametric method. The Raman spectra collected from the five uppermost micrometers of 11 healthy volunteers were fitted to different skin characteristics measured by independent appropriate methods (transepidermal water loss, hydration, pH, relative amount of ceramides, fatty acids, and cholesterol). For each parameter, the obtained PLS model presented correlation coefficients higher than R 2 =0.9 . This model enables us to obtain all the aforementioned parameters directly from the unique Raman signature. In addition to that, in-depth Raman analyses down to 20 μm showed different balances between partially bound water and unbound water with depth. In parallel, the increase of depth was followed by an unfolding process of the proteins. The combinations of all these information led to a multiparametric investigation, which better characterizes the skin status. Raman signal can thus be used as a quick response code (QR code). This could help dermatologic diagnosis of physiological variations and presents a possible extension to pathological characterization.

Paper Details

Date Published: 19 May 2014
PDF: 14 pages
J. Biomed. Opt. 19(11) 111603 doi: 10.1117/1.JBO.19.11.111603
Published in: Journal of Biomedical Optics Volume 19, Issue 11
Show Author Affiliations
Raoul Vyumvuhore, Univ. Paris-Sud 11 (France)
Ali M. Tfayli, Univ. Paris-Sud 11 (France)
Olivier Piot, Univ. de Reims Champagne-Ardenne (France)
Maud Le Guillou, SILAB (France)
Nathalie Guichard, SILAB (France)
Michel Manfait, Univ. de Reims Champagne-Ardenne (France)
Arlette Baillet-Guffroy, Univ. Paris-Sud 11 (France)

© SPIE. Terms of Use
Back to Top