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פותח על ידי קלירמאש פתרונות בע"מ -
Differentiation of Pectobacterium and Dickeya spp. phytopathogens using infrared spectroscopy and machine learning analysis
Year:
2020
Source of publication :
Journal of Biophotonics
Authors :
צרור, לאה
;
.
Volume :
13
Co-Authors:

Abu-Aqil, G., Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel; 
Shufan, E., Department of Physics, Shamoon College of Engineering, Beer-Sheva, Israel;
Adawi, S., Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel;
Mordechai, S., Department of Physics, Ben-Gurion University, Beer-Sheva, Israel;
Huleihel, M., Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel;
Salman, A., Department of Physics, Shamoon College of Engineering, Beer-Sheva, Israel

Facilitators :
From page:
1
To page:
10
(
Total pages:
10
)
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.

Note:
Related Files :
Agricultural crops
bacteria
Crops
infrared spectroscopy
Learning systems
Losses
Support vector machines
עוד תגיות
תוכן קשור
More details
DOI :
10.1002/jbio.201960156
Article number:
0
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
46380
Last updated date:
02/03/2022 17:27
Creation date:
01/03/2020 17:46
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Scientific Publication
Differentiation of Pectobacterium and Dickeya spp. phytopathogens using infrared spectroscopy and machine learning analysis
13

Abu-Aqil, G., Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel; 
Shufan, E., Department of Physics, Shamoon College of Engineering, Beer-Sheva, Israel;
Adawi, S., Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel;
Mordechai, S., Department of Physics, Ben-Gurion University, Beer-Sheva, Israel;
Huleihel, M., Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel;
Salman, A., Department of Physics, Shamoon College of Engineering, Beer-Sheva, Israel

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

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.

Scientific Publication
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