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A novel strategy for the rapid detection and identification 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 were used to establish a global Raman spectroscopy-based dendrogram model for Campylobacter identification to the species level and cross validated for its feasibility to predict Campylobacter-associated food-borne 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 the Campylobacter cell membrane. This Raman spectroscopy-based typing technique correlates well with multilocus sequence typing and has an average recognition rate of 97.21%. Discriminatory power for the Raman classification model had a Simpson index of diversity of 0.968. Intra- and interlaboratory reproducibility with different instrumentation yielded differentiation index values of 4.79 to 6.03 for wave numbers between 1,800 and 650 cm(-1) and demonstrated the feasibility of using this spectroscopic method at different laboratories. Our Raman spectroscopy-based partial least-squares regression model could precisely discriminate and quantify the actual concentration of a specific Campylobacter strain in a bacterial mixture (regression coefficient, >0.98; residual prediction deviation, >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 over traditional genotyping methods for bacterial epidemiology, such as detection speed and accuracy of identification to the species level.