Discrimination of Closely Related Vibrio Strains by Label-Free Surface-Enhanced Raman Spectroscopy (SERS)

Fish pathogens pose significant challenges to aquaculture, causing high mortality and economic losses. Thus, rapid, accurate, and cost-effective identification methods for disease management are needed. Conventional bacterial identification techniques are time-consuming and costly. Raman spectroscopy is an emerging alternative, enabling species differentiation based on molecular signatures. This study investigates surface-enhanced Raman spectroscopy (SERS) combined with principal component analysis with linear discriminant analysis (PCA-LDA) and partial least-squares discriminant analysis (PLS-DA) to classify three relevant Vibrio species: Vibrio alginolyticus, Vibrio parahaemolyticus, and Vibrio vulnificus. SERS spectra from bacterial cultures were analyzed to identify species-specific variations. Distinct clustering was observed, with V. vulnificus showing stronger amide-related signals, V. parahaemolyticus exhibiting higher sulfur-related Raman peaks, and V. alginolyticus showing distinct lipid region signals. The PCA-LDA model achieved 100% classification and 97.06% cross-validation accuracy under standardized laboratory conditions in this controlled proof-of-concept study using spectral replicates obtained from cultured laboratory bacterial stocks. The PLS-DA model showed excellent discriminatory performance (AUC = 1.00), with strong calibration for two species (R2 > 0.80) and slightly lower for V. alginolyticus (R2 = 0.71). These findings demonstrate that SERS combined with multivariate analysis is a promising approach for rapid and label-free Vibrio identification and classification under controlled laboratory conditions.