Differentiation of Pectobacterium and Dickeya spp. phytopathogens using infrared spectroscopy and machine learning analysis

George Abu-Aqil, Leah Tsror, Elad Shufan, Samar Adawi, Shaul Mordechai, Mahmoud Huleihel, Ahmad Salman

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Pectobacterium and Dickeya spp. are soft rot Pectobacteriaceae that cause aggressive diseases on agricultural crops leading to substantial economic losses. The accurate, rapid and low-cost detection of these pathogenic bacteria are very important for controlling their spread, reducing the consequent financial loss and for producing uninfected potato seed tubers for future generations. Currently used methods for the identification of these bacterial pathogens at the strain level are based mainly on molecular techniques, which are expensive. We used an alternative method, infrared spectroscopy, to measure 24 strains of five species of Pectobacterium and Dickeya. Measurements were then analyzed using machine learning methods to differentiate among them at the genus, species and strain levels. Our results show that it is possible to differentiate among different bacterial pathogens with a success rate of ~99% at the genus and species levels and with a success rate of over 94% at the strain level.

Original languageEnglish
Article numbere201960156
JournalJournal of Biophotonics
Volume13
Issue number5
DOIs
StatePublished - 1 May 2020

Keywords

  • linear support vector machine (lSVM)
  • quadratic support vector machine (qSVM)
  • soft rot Pectobacteriaceae (SRP)
  • vibrational spectroscopy

ASJC Scopus subject areas

  • Chemistry (all)
  • Materials Science (all)
  • Biochemistry, Genetics and Molecular Biology (all)
  • Engineering (all)
  • Physics and Astronomy (all)

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