Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Department of Plant Pathology, Institute of Plant Protection, Agricultural Research Organization, Gilat Research Center, Negev, Israel.
J Biophotonics. 2020 May;13(5):e201960156. doi: 10.1002/jbio.201960156. Epub 2020 Feb 16.
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.
果胶杆菌属和迪凯亚属是软腐果胶杆菌科，会导致农作物的侵袭性疾病，造成重大经济损失。准确、快速和低成本地检测这些病原菌对于控制其传播、减少相应的经济损失以及为后代生产无感染的马铃薯种薯非常重要。目前用于鉴定这些细菌病原菌在菌株水平上的方法主要基于分子技术，这些方法都很昂贵。我们使用了一种替代方法，即红外光谱法，来测量五种果胶杆菌属和迪凯亚属的 24 个菌株。然后使用机器学习方法对这些测量结果进行分析，以区分属、种和菌株水平。我们的结果表明，有可能以约 99%的成功率区分不同的细菌病原菌，在属和种水平上的成功率为 94%以上，在菌株水平上的成功率为 94%以上。
