The sugarcane aphid, Melanaphis sacchari, has been a severe pest throughout the sorghum field in Texas, which can worse the sorghum yield economically. For this purpose of early detection, the mechanism of herbivore-induced plant volatiles (HIPVs) needs to be utilized in the detection method. In this study, the HayeSep Q adsorbent combined gas chromatography mass spectrometry (GC/MS) was tested to analyze the volatile organic compounds (VOCs) that sorghum can emit when they are in good shape as well as they are infested by the sugarcane aphids, and multivariate techniques were performed for the fast screening of the infestation. Several VOCs identified from Student’s t-test with p < 0.05 were finally chosen as variables for multivariate analysis, and both unsupervised learning of principal component analysis (PCA) and clustering analysis (CA) and supervised learning of linear discriminant analysis (LDA) were done, showing good performance on discrimination between healthy and infested sorghum.

甘蔗蚜虫Melanaphis sacchari是德克萨斯州整个高粱田的一种严重害虫，这可能在经济上降低高粱的产量。为了早期检测，在检测方法中需要利用草食动物诱导的植物挥发物（HIPVs）的机理。在这项研究中，测试了HayeSep Q吸附剂组合气相色谱质谱（GC / MS），以分析高粱状态良好以及甘蔗蚜虫侵染时可散发出的挥发性有机化合物（VOC），并进行了多变量技术以快速筛查该病。从学生的t中识别出几种挥发性有机化合物最终选择p <0.05的X检验作为变量进行多元分析，并且进行了无监督学习的主成分分析（PCA）和聚类分析（CA）和监督学习的线性判别分析（LDA），显示出良好的区分能力在健康高粱和受害高粱之间。