Candida species are becoming one of the pathogens developing antifungal resistance due to inappropriate treatment and overuse of antimycotic drugs in building construction and agriculture. Further, fungal infections are often difficult to detect, also due to slow in vitro growth of the organisms from clinical specimens. Thus, fast detection and discrimination of yeast cells in direct patient materials is essential for an adequate treatment and success rate. In this work, we investigated Candida species isolated from patients, by using surface-enhanced Raman scattering (SERS) combined with computational spectroscopy tools, aiming to detect and discriminate between the three considered species, Candida albicans, Candida glabrata, and Candida parapsilosis. Density functional theory (DFT) was used to calculate Raman spectra of yeasts' main cell wall components for elucidating the origin of the observed bands. Accurate assignments of normal modes helped for a better understanding of the interaction between silver nanoparticles with yeasts' cell wall. Further, SERS spectra were used as samples in a database on which we performed multivariate analyses. By Principal component analysis (PCA), we obtained a maximum variation of 79% between the three samples. Linear discriminant analysis (LDA) was successfully used to discriminate between the three species.
Keywords: Candida species; Density functional theory; Linear discriminant analysis; Principal component analysis; Surface-enhanced Raman scattering.
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