数字农科院2.0

Nondestructive VOC-Based Phenotyping Strategy for Assessing Brown Planthopper Resistance at the Adult Stage in Rice

文献类型： 外文期刊

作者： Yang, Guiying;He, Jiachun;Song, Haoyan;Luo, Ju;Wang, Aiying;Yang, Baojun;Liu, Shuhua

作者机构：

关键词： rice resistance;brown planthopper;volatileorganic compounds (VOCs);machine learning;TD-GC/MS;support vector machine (SVM);PLS-DA

期刊名称： JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY

ISSN： 0021-8561

年卷期： 2025 年

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： The brown planthopper (BPH) is a major rice pest in Asia, with the most severe damage occurring at the adult stage in rice. Although breeding resistant varieties is key to pest control, current screening focuses mainly on seedlings using destructive, labor-intensive methods that may not reflect field performance. This study presents a volatile-based, nondestructive method to assess resistance at the adult stage in rice. Ten rice varieties were grouped as highly resistant (HR), resistant (R), or susceptible (S) based on seedling-stage bioassays and validated by field infestation levels. Volatile organic compounds (VOCs) emitted by adult plants were analyzed using thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS). Multivariate analysis identified five VOCs that differentiated resistance groups. These were used to train machine learning models, with support vector machine (SVM) achieving 0.89 accuracy. The results reveal clear VOC patterns associated with resistance, offering a reliable and scalable tool for adult-stage resistance screening to support breeding programs.

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