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Detection and identification of human fungal pathogens using surface-enhanced Raman spectroscopy and principal component analysis

Content Provider	Royal Society of Chemistry (RSC)
Author	Kamińska, Agnieszka Kowalska, Aneta Waluk, Jacek Hryncewicz-Gwóźdź, Anita Jagielski, Tomasz Witkowska, Evelin
Copyright Year	2016
Abstract	This paper demonstrates that surface-enhanced Raman spectroscopy (SERS) coupled with principal component analysis (PCA) can serve as a fast and reliable technique for the detection and identification of human fungal pathogens, such as Trichophyton rubrum, Candida krusei, Scopulariopsis brumptii, and Aspergillus flavus. Fungal infections have become one of the leading infectious causes of morbidity and mortality among hospitalized patients and/or immunocompromised hosts. Hence, there is a strong need for the development of new technologies allowing for fast and reliable diagnosis of fungal diseases. Our study shows that the SERS technique effectively distinguishes between selected common fungal pathogens and thus offers taxonomic affiliation of fungi within several minutes. Additionally, the PCA analysis allows performing statistical classification of fungal pathogens studied and identifying the fungal spectrum directly from a clinical sample. Calculated two principal components (PCs) (PC-1, PC-2) are the most diagnostically significant, explain 97% of the variability and enable, with very high probability, discrimination between the four mentioned fungal species. Moreover, the results of this study demonstrate the excellent possibility for the identification of fungi from human skin samples. The research presented in this paper offers an alternative for conventional fungal diagnostics and paves the way for the development of a new, fast, robust, and cost-effective diagnostic test for the detection and identification of fungal pathogens.
Starting Page	8427
Ending Page	8434
Page Count	8
File Format	HTM / HTML PDF
ISSN	17599660
Volume Number	8
Issue Number	48
Journal	Analytical Methods
DOI	10.1039/c6ay02957d
Language	English
Publisher	Royal Society of Chemistry
Access Restriction	Open
Subject Keyword	Candida albicans Scopulariopsis Aspergillus flavus Personal computer Disease Trichophyton Surface-enhanced Raman spectroscopy Immunodeficiency PCA Fungus Statistical classification PC-1 SERS Candida Principal component analysis
Content Type	Text
Resource Type	Article
Subject	Analytical Chemistry Engineering Chemical Engineering

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