Open AccessClassification of human skin Raman spectra using multivariate curve resolution (MCR) and partial least squares discriminant analysis (PLS-DA)
Author(s) -
И. А. Матвеева,
Yulia А. Khristoforova,
Alexander A. Moryatov,
Oleg O. Myakinin,
Ivan А. Bratchenko,
S Kozlov,
Valery P. Zakharov
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2127/1/012065
Subject(s) - linear discriminant analysis , pattern recognition (psychology) , raman spectroscopy , partial least squares regression , artificial intelligence , multivariate statistics , receiver operating characteristic , mathematics , resolution (logic) , analytical chemistry (journal) , statistics , chemistry , computer science , chromatography , optics , physics
The main purpose of the paper is classification of the human skin Raman spectra using partial least squares discriminant analysis (PLS-DA) into classes depending on the disease. In vivo Raman spectra of normal skin, basal cell carcinoma, malignant melanoma and pigmented nevus are considered. A feature of the approach is the analysis not of the Raman spectra themselves, but of the concentrations of the eight most significant spectra components identified using multivariate curve resolution (MCR). As a result, the ROC curve was calculated and the optimal classification threshold was chosen. The accuracy of the classification models ranged from 63.3 to 86.7%, depending on the model. The findings suggest that this approach could also be useful for classification of specific diseases.