Characterization of plant pathogenic bacteria at subspecies level using a dielectrophoresis device combined with Raman spectroscopy

Timely diagnosis of plant diseases and correct identification of etiological agents are fundamental to guarantee quality and quantity of agricultural products and food. Phytopathogenic bacteria induce devastating effects on crops. Their diagnosis and identification, mainly based on serological and molecular tools, are time consuming and expensive processes and require trained personnel. Among the innovative methods providing rapid, accurate, and reliable diagnosis at reduced costs, Raman spectroscopy (RS) is gathering considerable attention. RS provides a direct and non-destructive platform to gather information on the chemical and biochemical components of a sample, such as microorganism cultures, revealing their biological role. Due to the weak signals of bacterial cells in RS, a dielectrophoresis (DEP) approach was adopted to amplify the bacterial signals. Using Raman-DEP analysis, a dataset of spectra from different harmful phytopathogenic bacteria belonging to the genera Pseudomonas spp., Xanthomonas spp., and Erwinia spp. was obtained. Machine learning approaches were employed to discriminate isolates at the genus, species, and unprecedentedly at the pathovar level, reaching accuracies, precisions, recalls, and F1 scores of 94–100%. This approach offers important advancements in the non-destructive and rapid classification of microorganisms and is suitable to be readily extended to environmental and food diagnostics. • Dielectrophoresis-aided Raman microscopy allows fast identification of bacteria. • Pathovars of plant pathogens were reliably identified with Raman for the first time. • The machine learning model had 100% performance on genus and species prediction. • Sub-species identification had excellent performance as well (94–100%) in minutes. • Possible applications: sub-species level analysis on plant and animal bacteria.