Ceftriaxone-Mediated SERS Profiling Combined with Machine Learning for Rapid Identification of Antimicrobial Resistance in Klebsiella pneumoniae : An Exploratory Study

The third-generation class of cephalosporins is the most commonly prescribed group of antibiotics. The cephalosporin is the latest drug against gram-negative bacteria, which works by inhibiting the cell wall of bacteria. They are used to treat various infections worldwide. Surface-enhanced Raman spectroscopy (SERS) has been utilized for the detection of the effect of 3rd generation cephalosporin on K. pneumoniae bacteria. SERS using silver nanoparticles as a substrate has great potential to provide a highly sensitive platform for studying bacterial responses to antibiotics at the molecular level. Chemometric techniques are utilized for the determination of the effect of cephalosporin on sensitive and resistant strains of K. pneumoniae. Principal component analysis (PCA) and K nearest neighbor model (KNN) have been used for differentiation of biochemical changes caused by exposure to cephalosporin on sensitive and resistant strains of K. pneumoniae. The binary classification yielded from the K-nearest neighbor model has produced a perfectly discriminative confusion matrix, with sensitivity and specificity both reaching 100%. The integration of PCA with KNN enhances the accuracy of strain classification by combining dimensionality reduction with a simple yet effective classification algorithm. This approach demonstrates the potential of SERS combined with machine learning as a rapid, noninvasive diagnostic tool for monitoring antibiotic response in clinical microbiology.