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.

中文翻译：

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果胶杆菌和迪卡氏菌的鉴别。植物病原体利用红外光谱和机器学习分析。

果胶杆菌和迪卡酵母属。是软腐菌科，会在农作物上引起侵略性疾病，从而造成重大经济损失。对这些病原菌的准确，快速和低成本的检测对于控制其传播，减少随之而来的经济损失以及为后代生产未感染的马铃薯种薯非常重要。当前在菌株水平上鉴定这些细菌病原体的方法主要基于分子技术，这很昂贵。我们使用了另一种方法，即红外光谱法，测量了五种果胶杆菌和迪卡菌属的24个菌株。然后使用机器学习方法对测量进行分析，以区分属，种和品系水平。我们的结果表明，可以区分不同细菌病原体，在属和种水平上的成功率约为99％，在菌株水平上的成功率超过94％。