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

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, classi

SUBMITTER: Highmore C 

PROVIDER: S-EPMC12378394 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature