Identification and antimicrobial resistance profiling of Pseudomonas aeruginosa using multi-excitation Raman spectroscopy and computational analytics

Antimicrobial resistance (AMR) poses a global healthcare challenge, where overprescription of antibiotics contributes to its prevalence. We have developed a rapid multi-excitation Raman spectroscopy methodology (MX-Raman) that outperforms conventional Raman spectroscopy and enhances specificity. A support vector machine (SVM) model was used to identify 20 clinical isolates of Pseudomonas aeruginosa with an accuracy of 93% using MX-Raman. Antibiotic sensitivity profiles for tobramycin, ceftazidime, ciprofloxacin, and imipenem were generated for the bacterial strains and compared with their Raman spectral signatures using MX-Raman. The 20 clinical strains were distinguished according to AMR profiles. Nine models were assessed for AMR classification performance, and SVM performed best, classifying AMR profiles of each strain with 91-96% accuracy. These data provide the basis for a new rapid clinical diagnostic platform that could screen for bacterial infection and recommend effective antibiotic treatment ahead of confirmation by conventional techniques, improving clinical outcomes and reducing the spread of AMR.

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Publication Details

Journal
npj Antimicrobials and Resistance
Published
2025-08-25
DOI
https://doi.org/10.1038/s44259-025-00141-z
Citations
2
Primary Topic
Spectroscopy Techniques in Biomedical and Chemical Research
Type
article
Field-Weighted Citation Impact
3.27

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Identification and antimicrobial resistance profiling of Pseudomonas aeruginosa using multi-excitation Raman spectroscopy and computational analytics

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npj Antimicrobials and Resistance
Spectroscopy Techniques in Biomedical and Chemical Research
3.27
article

Identification and antimicrobial resistance profiling of Pseudomonas aeruginosa using multi-excitation Raman spectroscopy and computational analytics

George Devitt, Adam P. Lister, Sumeet Mahajan, Alastair Munro, Niall Hanrahan, Jeremy S. Webb, Ysanne Pritchard, Callum Highmore, Y. Cook, K Cooper, Saul N. Faust
article en
2 citations

Abstract

Antimicrobial resistance (AMR) poses a global healthcare challenge, where overprescription of antibiotics contributes to its prevalence. We have developed a rapid multi-excitation Raman spectroscopy methodology (MX-Raman) that outperforms conventional Raman spectroscopy and enhances specificity. A support vector machine (SVM) model was used to identify 20 clinical isolates of Pseudomonas aeruginosa with an accuracy of 93% using MX-Raman. Antibiotic sensitivity profiles for tobramycin, ceftazidime, ciprofloxacin, and imipenem were generated for the bacterial strains and compared with their Raman spectral signatures using MX-Raman. The 20 clinical strains were distinguished according to AMR profiles. Nine models were assessed for AMR classification performance, and SVM performed best, classifying AMR profiles of each strain with 91-96% accuracy. These data provide the basis for a new rapid clinical diagnostic platform that could screen for bacterial infection and recommend effective antibiotic treatment ahead of confirmation by conventional techniques, improving clinical outcomes and reducing the spread of AMR.

npj Antimicrobials and ResistanceVol. 3(1)
University Hospital Southampton NHS Foundation Trust (GB), NIHR Southampton Biomedical Research Centre (GB), University of Southampton (GB)
National Institute for Health and Care Research, Norwegian Biodiversity Information Centre, National Institute for Health Research Southampton Biomedical Research Centre, Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council
Openalex Percentile: Top 7%
Spectroscopy Techniques in Biomedical and Chemical Research
3.27
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