Differentiation of Pectobacterium and Dickeya spp. phytopathogens using infrared spectroscopy and machine learning analysis.
linear support vector machine (lSVM)
quadratic support vector machine (qSVM)
soft rot Pectobacteriaceae (SRP)
vibrational spectroscopy
Journal
Journal of biophotonics
ISSN: 1864-0648
Titre abrégé: J Biophotonics
Pays: Germany
ID NLM: 101318567
Informations de publication
Date de publication:
05 2020
05 2020
Historique:
received:
17
10
2019
revised:
10
12
2019
accepted:
01
02
2020
pubmed:
8
2
2020
medline:
24
6
2021
entrez:
8
2
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32030907
doi: 10.1002/jbio.201960156
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e201960156Informations de copyright
© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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