Nakar A, Wagenhaus A, Roesch P, Popp J (2022)
Publication Type: Journal article
Publication year: 2022
Book Volume: 147
Pages Range: 3938-3946
Journal Issue: 17
DOI: 10.1039/d2an00822j
Enterobacteriaceae are the leading cause of urinary tract infections, and include pathogens such as E. coli, K. pneumoniae and P. mirabilis. Due to their similarity, the correct identification of these pathogens is difficult and time-consuming. Raman spectroscopy has been demonstrated extensively as a tool for rapid microbiological differentiation. However, for pathogenic Enterobacteriaceae the application of Raman spectroscopy has been particularly challenging. In this study, two promising methods for Raman-based microbiological diagnostics were compared for differentiating Enterobacteriaceae. Spectra were collected from single-cells with Raman microspectroscopy and from colonies on agar with an NIR Raman fiber-probe. A comprehensive dataset of spectra from 8 different, clinically relevant, genera was collected. Visually, the spectra obtained from both methods presented little difference between the genera. For classification, single cell analysis yielded limited results, while the fiber-probe spectra enabled perfect classification of all 16 isolates. Moreover, the model was validated on new replicates and 15/16 strains were correctly identified (94% overall accuracy). This is the first study to focus on the closely related Enterobacteriaceae, who have previously been avoided or differentiated poorly. It shows how, with the correct spectroscopic setup, even challenging questions in clinical microbiology can be resolved with Raman spectroscopy, highlighting the method's potential for improving patient care.
APA:
Nakar, A., Wagenhaus, A., Roesch, P., & Popp, J. (2022). Raman spectroscopy for the differentiation of Enterobacteriaceae: a comparison of two methods. Analyst, 147(17), 3938-3946. https://doi.org/10.1039/d2an00822j
MLA:
Nakar, Amir, et al. "Raman spectroscopy for the differentiation of Enterobacteriaceae: a comparison of two methods." Analyst 147.17 (2022): 3938-3946.
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