Rapid and accurate identification of Arcobacter is of great importance because it has been considered as an emerging food- and water-borne pathogen and potential zoonotic agent. Raman spectroscopy can differentiate bacteria based on Raman scattering spectral patterns of whole cells in a fast, reagentless, and easy-to-use manner. We aimed to detect and discriminate Arcobacter at the species level using confocal micro-Raman spectroscopy (785 nm) coupled with neural networks. A total of 82 reference and field isolates of 18 Arcobacter species from clinical, environmental and agri-food sources were included. We determined that the bacterial cultivation time and temperature did not significantly influence the Raman spectral reproducibility and discrimination capability. The genus Arcobacter could be successfully differentiated from the closely related genera Campylobacter and Helicobacter using principal component analysis. For the identification of Arcobacter to the species level, an accuracy of 97.2% was achieved for all 18 Arcobacter species using Raman spectroscopy combined with convolutional neural network (CNN). The predictive capability of Raman-CNN was further validated using an independent dataset of 12 Arcobacter strains. Furthermore, a Raman spectroscopic-based fully-connected artificial neural network (ANN) was constructed to determine the actual ratio of a specific Arcobacter species in a bacterial mixture ranging from 5% to 100% by biomass (regression coefficient >0.99). The application of both CNN and fully-connected ANN improved the accuracy of using Raman spectroscopy for bacterial speciation compared to the conventional chemometrics. This newly developed approach enables rapid identification and speciation of Arcobacter within an hour following cultivation. IMPORTANCE Rapid identification of bacterial pathogens is critical to develop an early warning system and performing epidemiological investigation. Arcobacter is an emerging foodborne pathogen and becomes more important in the recent decades. The incidence of Arcobacter species in the agro-ecosystem is probably underestimated mainly due to the limitation in the available detection and characterization techniques. Raman spectroscopy combined with machine learning can accurately identify Arcobacter at species level in a rapid and reliable manner, providing a promising tool for epidemiological surveillance of this microbe in the agri-food chain. The knowledge received from this study has the potential to be used as a standard for routine bacterial screening and diagnostics by the government, food industry and clinics.

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