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

• Published in: Journal of biophotonics 2020-05, Vol.13 (5), p.e201960156-n/a
• Main Authors: Abu‐Aqil, George, Tsror, Leah, Shufan, Elad, Adawi, Samar, Mordechai, Shaul, Huleihel, Mahmoud, Salman, Ahmad
• Format: Article
• Language: English
• Subjects: Agricultural economics; Agronomy; Bacteria; Economic impact; Identification methods; Infrared analysis; Infrared spectroscopy; Learning algorithms; Levels; linear support vector machine (lSVM); Machine learning; Pathogens; Pectobacterium; Potatoes; quadratic support vector machine (qSVM); Soft rot; soft rot Pectobacteriaceae (SRP); Species; Spectrum analysis; Tubers; vibrational spectroscopy
• ISSN: 1864-063X; 1864-0648
• DOI: 10.1002/jbio.201960156

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. SRP 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 and reducing the consequent financial loss. Early detection is also highly important for producing uninfected potato seed tubers for future generations. In this study, we successfully used infrared spectroscopy combined with different machine learning classifiers to differentiate among SRP strains.