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Detection of Huanglongbing Disease Using Differential Mobility Spectrometry

机译:差动光谱法检测黄龙病

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The viability of the multibillion dollar global citrus industry is threatened by the "green menace", citrus greening disease (Huanglongbing, HLB), caused by the bacterial pathogen Candidatus Liberibacter. The long asymptomatic stage of HLB makes it challenging to detect emerging regional infections early to limit disease spread. We have established a novel method of disease detection based on chemical analysis of released volatile organic compounds (VOCs) that emanate from infected trees. We found that the biomarkers "fingerprint" is specific to the causal pathogen and could be interpreted using analytical methods such as gas chromatography/mass spectrometry (GC/MS) and gas chromatography/differential mobility spectrometry (GC/DMS). This VOC-based disease detection method has a high accuracy of ~90% throughout the year, approaching 100% under optimal testing conditions, even at very early stages of infection where other methods are not adequate. Detecting early infection based on VOCs precedes visual symptoms and DNA-based detection techniques (real-time polymerase chain reaction, RT-PCR) and can be performed at a substantially lower cost and with rapid field deployment.
机译:数十亿美元的全球柑橘产业的生存受到细菌病原体假丝酵母念珠菌引起的“绿色威胁”,即柑橘绿化病(黄龙病,HLB)的威胁。 HLB的长期无症状期使其难以及早发现新出现的区域感染以限制疾病传播。我们基于对受感染树木释放的挥发性有机化合物(VOC)的化学分析,建立了一种新的疾病检测方法。我们发现生物标志物“指纹”是特定于病原体的,可以使用分析方法(例如气相色谱/质谱(GC / MS)和气相色谱/差动迁移谱(GC / DMS))进行解释。这种基于VOC的疾病检测方法,全年精度高达〜90%,在最佳测试条件下,甚至在其他方法都不充分的感染的早期阶段,也能达到100%。在视觉症状和基于DNA的检测技术(实时聚合酶链反应,RT-PCR)之前,基于VOC的早期感染检测可以大大降低成本并迅速部署到现场。

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