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- W3048657004 abstract "Rapid identification of bacterial pathogens is critical for developing an early warning system and performing epidemiological investigation. Arcobacter is an emerging foodborne pathogen and has become more important in 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 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." @default.
- W3048657004 created "2020-08-18" @default.
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- W3048657004 date "2020-10-01" @default.
- W3048657004 modified "2023-10-07" @default.
- W3048657004 title "<i>Arcobacter</i> Identification and Species Determination Using Raman Spectroscopy Combined with Neural Networks" @default.
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- W3048657004 doi "https://doi.org/10.1128/aem.00924-20" @default.
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