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JOURNALS // Kvantovaya Elektronika // Archive

Kvantovaya Elektronika, 2019 Volume 49, Number 1, Pages 6–12 (Mi qe16960)

This article is cited in 11 papers

Laser biophotonics

Confocal Raman imaging of skin sections containing hair follicles using classical least squares regression and multivariate curve resolution–alternating least squares

J. Schleusenerab, V. Carrerabc, A. Patzeltab, S. Guode, T. Bocklitzde, L. Coderchc, J. Lademannab, M. E. Darvinab

a Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Germany
b Berlin Institute of Health, Department of Dermatology, Venerology and Allergology, Center of Experimental and Applied Cutaneous Physiology, Germany
c Institute of Advanced Chemistry of Catalonia, Department of Chemical and Surfactants Technology, Spain
d Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Germany
e Leibniz Institute of Photonic Technology, Member of Leibniz Research Alliance "Health Technologies", Germany

Abstract: Confocal Raman microscopy (CRM) is applied ex vivo for imaging of the spatial distribution of different skin components in skin sections containing hair follicles. For multivariate data analysis, different methods are used in order to spectrally decompose the reference spectra of the skin components (dermis, viable epidermis, stratum corneum and hair). Classical least squares regression (CLS) and multivariate curve resolution–alternating least squares (MCR-ALS) are chosen as suitable methods. In comparison to other optical methods, the advantage of CRM is molecular specificity and dispensability of labelling dyes, which is e.g. necessary in fluorescence microscopy. Therefore, a useful future application of CRM in combination with multivariate data analysis lies in the analysis of penetration routes of topically applied substances, such as cosmetic formulations or drugs into the skin, which is particularly interesting in and around hair follicles.

Keywords: dermatology, optical profilometry, multivariate data analysis, hyperspectral imaging, skin imaging, confocal Raman microscopy.

Received: 25.09.2018
Revised: 12.10.2018


 English version:
Quantum Electronics, 2019, 49:1, 6–12

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