UV-Raman spectroscopic identification of fungal spores important for respiratory diseases

Title data

Žukovskaja, Olga ; Kloß, Sandra ; Blango, Matthew G. ; Ryabchykov, Oleg ; Kniemeyer, Olaf ; Brakhage, Axel A. ; Bocklitz, Thomas ; Cialla-May, Dana ; Weber, Karina ; Popp, Jürgen:
UV-Raman spectroscopic identification of fungal spores important for respiratory diseases.
In: Analytical Chemistry. Vol. 90 (2018) Issue 15 . - pp. 8912-8918.
ISSN 1520-6882
DOI: https://doi.org/10.1021/acs.analchem.8b01038

Official URL:

Abstract in another language

Fungal spores are one of several environmental factors responsible for causing respiratory diseases like asthma, chronic obstructive pulmonary disease (COPD), and aspergillosis. These spores also are able to trigger exacerbations during chronic forms of disease. Different fungal spores may contain different allergens and mycotoxins, therefore the health hazards are varying between the species. Thus it is highly important quickly to identify the composition of fungal spores in the air. In this study, UV-Raman spectroscopy with an excitation wavelength of 244 nm was applied to investigate eight different fungal species implicated in respiratory diseases worldwide. Here, we demonstrate that darkly colored spores can be directly examined, and UV-Raman spectroscopy provides the information sufficient for classifying fungal spores. Classification models on the genus, species, and strain levels were built using a combination of principal component analysis (PCA) and linear discriminant analysis (LDA) followed by evaluation with leave-one-batch-out-cross-validation (LBOCV). At the genus level an accuracy of 97.5 was achieved, whereas on the species level four different Aspergillus species were classified with 100 accuracy. Finally, classifying three strains of Aspergillus fumigatus an accuracy of 89.4 was reached. These results demonstrate that UV-Raman spectroscopy in combination with innovative chemometrics allows for fast identification of fungal spores and can be a potential alternative to currently used time-consuming cultivation. Fungal spores are one of several environmental factors responsible for causing respiratory diseases like asthma, chronic obstructive pulmonary disease (COPD), and aspergillosis. These spores also are able to trigger exacerbations during chronic forms of disease. Different fungal spores may contain different allergens and mycotoxins, therefore the health hazards are varying between the species. Thus it is highly important quickly to identify the composition of fungal spores in the air. In this study, UV-Raman spectroscopy with an excitation wavelength of 244 nm was applied to investigate eight different fungal species implicated in respiratory diseases worldwide. Here, we demonstrate that darkly colored spores can be directly examined, and UV-Raman spectroscopy provides the information sufficient for classifying fungal spores. Classification models on the genus, species, and strain levels were built using a combination of principal component analysis (PCA) and linear discriminant analysis (LDA) followed by evaluation with leave-one-batch-out-cross-validation (LBOCV). At the genus level an accuracy of 97.5 was achieved, whereas on the species level four different Aspergillus species were classified with 100 accuracy. Finally, classifying three strains of Aspergillus fumigatus an accuracy of 89.4 was reached. These results demonstrate that UV-Raman spectroscopy in combination with innovative chemometrics allows for fast identification of fungal spores and can be a potential alternative to currently used time-consuming cultivation.

Further data

Item Type:	Article in a journal
Refereed:	Yes
Institutions of the University:	Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz
Result of work at the UBT:	No
DDC Subjects:	500 Science > 530 Physics
Date Deposited:	12 May 2023 07:03
Last Modified:	12 May 2023 07:03
URI:	https://eref.uni-bayreuth.de/id/eprint/76387