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Rapid classification of bacteria by a portable Raman spectrometer and machine learning.

Camilla Sacco Botto1, Ciro Orecchio2, Chiara D'Errico1

  • 1Institute for Sustainable Plant Protection, National Research Council of Italy, Strada delle Cacce 73, 10135 Torino, Italy.

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|July 27, 2025
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Summary

Rapidly identifying plant pathogens using Raman spectroscopy (RS) and machine learning (ML) aids crop disease management. This study shows handheld RS with ML accurately differentiates bacterial genera like Pseudomonas, Xanthomonas, and Erwinia.

Keywords:
BacteriaMachine learningPhytopathogensRaman spectroscopyTaxonomic identification

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Area of Science:

  • Agricultural Science
  • Microbiology
  • Analytical Chemistry

Background:

  • Early detection of plant pathogens is vital for food security and disease control.
  • Raman spectroscopy (RS) and machine learning (ML) offer potential for rapid diagnostics.

Purpose of the Study:

  • To evaluate a handheld Raman spectroscopy (RS) instrument combined with machine learning (ML) for identifying plant pathogenic bacteria.
  • To differentiate bacterial isolates from the genera Pseudomonas, Xanthomonas, and Erwinia.

Main Methods:

  • Acquired 332 Raman spectra directly from bacterial colonies on solid media.
  • Compared various machine learning algorithms for spectral analysis.
  • Developed Partial Least Squares Discriminant Analysis (PLS-DA) models.

Main Results:

  • Achieved high accuracy (83%–99%) in identifying bacterial genera, species, and pathovars.
  • Demonstrated the effectiveness of handheld RS coupled with ML for bacterial differentiation.
  • Validated the performance of PLS-DA models.

Conclusions:

  • This approach enables fast, non-destructive classification of plant pathogenic bacteria.
  • Facilitates the creation of accessible spectral databases for microbial identification.
  • Supports timely prevention and management of crop diseases.