2018
DOI: 10.1021/acs.analchem.7b03124
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Characterization of Clinically Relevant Fungi via SERS Fingerprinting Assisted by Novel Chemometric Models

Abstract: Nonculture-based tests are gaining popularity and upsurge in the diagnosis of invasive fungal infections (IFI) fostered by their main asset, the reduced analysis time, which enables a more rapid diagnosis. In this project, three different clinical isolates of relevant filamentous fungal species were discriminated by using a rapid (less than 5 min) and sensitive surface-enhanced Raman scattering (SERS)-based detection method, assisted by chemometrics. The holistic evaluation of the SERS spectra was performed by… Show more

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Cited by 49 publications
(36 citation statements)
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“…This approach enhances clinical applicability of SERS as demonstrated for accurate identification and classification of clinical isolates of E. coli, Bacillus spp. [120,121], opportunistic Staphylococcus epidermidis [17], Aspergillus fumigatus and Rhizomucor pusillus [122], and to probe microbial cell functionality [119]. Intriguingly, an in situ SERS method proved to be very sensitive and effective for susceptibility assessment of clinical pathogens against common first line antibiotics treatment [17], to complement previous efforts [18,123].…”
Section: Microbial Infections-pathogen Detectionmentioning
confidence: 99%
“…This approach enhances clinical applicability of SERS as demonstrated for accurate identification and classification of clinical isolates of E. coli, Bacillus spp. [120,121], opportunistic Staphylococcus epidermidis [17], Aspergillus fumigatus and Rhizomucor pusillus [122], and to probe microbial cell functionality [119]. Intriguingly, an in situ SERS method proved to be very sensitive and effective for susceptibility assessment of clinical pathogens against common first line antibiotics treatment [17], to complement previous efforts [18,123].…”
Section: Microbial Infections-pathogen Detectionmentioning
confidence: 99%
“…After spectral pre-processing, a principal component analysis (PCA) model was combined with a support vector machine (SVM) model in order to reduce the high dimensionality of Raman spectra and to differentiate between the pathogenic and non-pathogenic bacteria. In this context, different chemometric techniques can be also implemented for feature selection and pathogenicity identification, e.g., biomolecular component analysis [ 16 ] and the combination of fuzzy principal component analysis or principal component analysis with linear discriminant analysis [ 17 , 18 ]. In our work, the PCA-SVM model was trained on the 14 cultivated E. coli strains while the pathogenicity of the E. coli isolates was predicted using the trained model.…”
Section: Methodsmentioning
confidence: 99%
“…SERS combined with PCA was used to detect and identify human fungal pathogens rapidly and reliably. Dina et al [ 173 ] distinguished different clinical samples of fungal species using the chemometrics assisted SERS-based method. The overall analysis of the SERS spectra was carried out using appropriate chemometric tools-classical and fuzzy PCA combined with linear discriminant analysis to analysis the first principal components.…”
Section: Application On the Detection Of Microorganism Original Bimentioning
confidence: 99%