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 employing appropriate chemometric tools-classical and fuzzy principal component analysis (FPCA) in combination with linear discriminant analysis (LDA) applied to the first relevant principal components. The efficiency of the proposed robust algorithm is illustrated on the data set including three fungal isolates (Aspergillus fumigatus sensu stricto, cryptic A. fumigatus complex species, and Rhizomucor pusillus) that were isolated from patient materials. The accurate and reliable discrimination between species of common fungal pathogen strains suggest that the developed method has the potential as an alternative, spectroscopic-based routine analysis tool in IFI diagnosis.

Enhancing Disease Diagnosis: Biomedical Applications of Surface-Enhanced Raman Scattering

“…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].…”

Discrimination between pathogenic and non-pathogenic E. coli strains by means of Raman microspectroscopy

“…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.…”

Analysis of Biomolecules Based on the Surface Enhanced Raman Spectroscopy

“…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.…”