Rapid In Situ Analysis of Plant Emission for Disease Diagnosis Using a Portable Gas Chromatography Device
Rapid In Situ Analysis of Plant Emission for Disease Diagnosis Using a Portable Gas Chromatography Device
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DOI：
10.1021/acs.jafc.9b02500
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发表时间：
2019-07-03
影响因子：
6.1
通讯作者：
Fan, Xudong
中科院分区：
文献类型：
--
作者：
Sharma, Ruchi;Zhou, Menglian;Fan, Xudong
我们开发并应用了一种全自动便携式气相色谱(GC)设备，用于快速和现场分析植物挥发性有机化合物(VOCs)，以检查植物的健康状况。在5天的时间里，共采集了10株马尾藻(Asclepias Syriaca)的42个排放样本，其中有一半的植物受到了蚜虫的侵染。在8min内分离并检测到35个VOC峰。提出了一种基于机器学习、主成分分析和线性判别分析的GC结果评价算法。我们发现，我们的设备和算法能够在攻击后48-72小时内区分未受损害的对照和有蚜虫侵染的米象，总体准确率为90%-100%。如此快速的现场检测虫害，证明了VOC监测在植物健康管理中的巨大潜力。
We developed and applied a fully automated portable gas chromatography (GC) device for rapid and in situ analysis of plant volatile organic compounds (VOCs) to examine plant health status. A total of 42 emission samples were collected over a period of 5 days from 10 milkweed (Asclepias syriaca) plants, half of which were infested by aphids. Thirty-five VOC peaks were separated and detected in 8 min. An algorithm based on machine learning, principal component analysis, and linear discriminant analysis was developed to evaluate the GC results. We found that our device and algorithm are able to distinguish between the undamaged control and the aphid-infested milkweeds with an overall accuracy of 90-100% within 48-72 h of the attack. Such rapid in situ detection of insect attack attests to the great potential of VOC monitoring in plant health management.