Label‐free differentiation of clinical E. coli and Klebsiella isolates with Raman spectroscopy

GND
1298477557
ORCID
0000-0002-1342-7471
Affiliation
Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Jena, Germany
Nakar, Amir;
GND
1244950157
Affiliation
Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Jena, Germany
Pistiki, Aikaterini;
GND
1194391788
ORCID
0000-0002-4655-8080
Affiliation
Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Jena, Germany
Ryabchykov, Oleg;
GND
101788207X
ORCID
0000-0003-2778-6624
Affiliation
Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Jena, Germany
Bocklitz, Thomas;
GND
124507387
ORCID
0000-0001-6179-3719
Affiliation
Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena Jena Germany
Rösch, Petra;
GND
131701819
ORCID
0000-0003-4257-593X
Affiliation
Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Jena, Germany
Popp, Jürgen

Raman spectroscopy is a promising spectroscopic technique for microbiological diagnostics. In routine diagnostic, the differentiation of pathogens of the Enterobacteriaceae family remain challenging. In this study, Raman spectroscopy was applied for the differentiation of 24 clinical E. coli , Klebsiella pneumoniae and Klebsiella oxytoca isolates. Spectra were collected with two spectroscopic approaches: UV‐Resonance Raman spectroscopy (UVRR) and single‐cell Raman microspectroscopy with 532 nm excitation. A description of the different biochemical profiles provided by the different excitation wavelengths was performed followed by machine‐learning models for the classification at the genus and species levels. UVRR was shown to outperform 532 nm excitation, enabling correct classification at the genus level of 23/24 isolates. Furthermore, for the first time, Klebsiella species were correctly classified at the species level with 92% accuracy, classifying all three K. oxytoca isolates correctly. These findings should guide future applicative studies, increasing the scope of Raman spectroscopy's suitability for clinical applications.

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