Technical Abstract: A novel strategy for rapid identification and speciation of traditional and emerging Campylobacter strains based upon Raman spectroscopy (532 nm) is presented here. A total of 200 reference strains and clinical isolates of 11 different Campylobacter species recovered from infected animals and humans from China and North America was used to establish a global Raman spectroscopy-based dendrogram model for Campylobacter speciation and cross-validated for its feasibility to predict Campylobacter-associated foodborne outbreaks. Bayesian probability coupled with Monte-Carlo estimation was employed to validate the established Raman classification model on the basis of the selected principal components, mainly protein secondary structures on Campylobacter cell membrane. This Raman spectroscopy-based typing technique correlates well with multilocus sequence typing (MLST) and has an average recognition rate of 97.21%. Discriminatory power for the Raman classification model had a Simpson's index of diversity of 0.968. Intra- and inter-laboratory reproducibility using different instrumentation yielded Dy1y2 values of 4.79-6.03 for wavenumbers between 1800-650 cm-1 and demonstrated the feasibility of performing this method at different laboratories. Raman spectroscopy-based partial least squares regression (PLSR) model could precisely discriminate and quantify the actual concentration of a specific Campylobacter strain in a bacterial mixture [regression coefficient (R) > 0.98 and residual prediction deviation (RPD) > 7.88]. A standard protocol for sample preparation, spectral collection, model validation and data analyses was established for the Raman spectroscopic technique. Raman spectroscopy may have advantages compared to traditional genotyping methods for bacterial epidemiology, such as detection speed and speciation accuracy.