# Label-Free Differentiation of Antimicrobial Resistance Groups Using Raman Spectroscopy

## TL;DR

This study explores using Raman spectroscopy to distinguish antimicrobial resistance classes in bacteria, showing promising results for hospital diagnostics.

## Contribution

The paper introduces a 3-step Raman spectroscopy-based decision-tree approach for classifying species, resistance status, and AMR subclasses in clinical isolates.

## Key findings

- 785 nm Raman excitation outperformed UVRR in differentiating susceptible and resistant strains within species.
- PCA-SVM and PLS-DA provided the highest discriminative power for ESBL vs CRE classification.
- Raman spectroscopy using 785 nm excitation is suitable for hospital diagnostic applications.

## Abstract

Increasing antimicrobial resistance (AMR) has developed
into an
enormous health burden. Here, a systematic investigation was conducted
to evaluate the discriminative performance of Raman spectroscopy between
different resistance classes (Susceptible, ESBL, CRE, VRE, VSE) in
common clinical isolates (Escherichia coli, Klebsiella pneumoniae, Klebsiella oxytoca, Citrobacter freundii, Acinetobacter baumanii, Enterococcus faecium). Two different Raman spectroscopic
methods (UVRR in bulk and 785 nm excitation directly on the Petri
dish) and four different machine learning algorithms (PCA-LDA, PLS-DA,
PCA-SVM, PCA-RF) were tested aiming the application of a decision-tree
using a 3-step approach composing of species classification, differentiation
of susceptible from resistant strains within the species and differentiation
of ESBL and CRE as AMR subclasses within the class of antibiotic-resistant
strains. In species classification, the two Raman methods yield similar
results in all applied models. When attempting the differentiation
of susceptible vs resistant strains in the intraspecies level, 785
nm overall outperformed UVRR and PCA-SVM and PLS-DA provided higher
discriminative power compared to PCA-LDA and PCA-RF. For the discrimination
of ESBL vs CRE isolates UVRR was not suitable as a method and 785
nm excitation provided correct identification of all 9 strains when
using PCA-SVM and PLS-DA, confirming stability over replicate-to-replicate
variations. Raman spectra from 785 nm excitation directly on the Petri
dish combined with PCA-SVM and PLS-DA are suitable for diagnostic
application of Raman spectroscopy in hospital settings. These results
are the first step of a long journey in the development of Raman spectroscopy
for microbiological documentation and extraction of AMR-related information
in infectious diseases.