<i>Arcobacter</i> Identification and Species Determination Using Raman Spectroscopy Combined with Neural Networks

Abstract

Rapid and accurate identification of <i>Arcobacter</i> is of great importance because it is considered an emerging food- and waterborne 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 <i>Arcobacter</i> bacteria 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 <i>Arcobacter</i> species from clinical, environmental, and agri-food sources were included. We determined that the bacterial cultivation time and growth temperature did not significantly influence the Raman spectral reproducibility and discrimination capability. The genus <i>Arcobacter</i> could be successfully differentiated from the closely related genera <i>Campylobacter</i> and <i>Helicobacter</i> using principal-component analysis. For the identification of <i>Arcobacter</i> to the species level, an accuracy of 97.2% was achieved for all 18 <i>Arcobacter</i> species using Raman spectroscopy combined with a convolutional neural network (CNN). The predictive capability of Raman-CNN was further validated using an independent data set of 12 <i>Arcobacter</i> strains. Furthermore, a Raman spectroscopy-based fully connected artificial neural network (ANN) was constructed to determine the actual ratio of a specific <i>Arcobacter</i> 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 Raman spectroscopy for bacterial species determination compared to the conventional chemometrics. This newly developed approach enables rapid identification and species determination of <i>Arcobacter</i> within an hour following cultivation.<b>IMPORTANCE</b> Rapid identification of bacterial pathogens is critical for developing an early warning system and performing epidemiological investigation. <i>Arcobacter</i> is an emerging foodborne pathogen and has become more important in recent decades. The incidence of <i>Arcobacter</i> 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 <i>Arcobacter</i> at the 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 elicited from this study has the potential to be used for routine bacterial screening and diagnostics by the government, food industry, and clinics.