您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

HS-GC-IMS couples with convolutional neural network for Burkholderia gladioli pv. Cocovenenans detection in Auricularia Auricula

文献类型： 外文期刊

作者： Niu, Chen 1 ; Zhao, Mincheng 2 ; Sheng, Qinglin 1 ; Wu, Yuanchun 1 ; Song, Zihan 3 ; Wei, Jianping 1 ; Yuan, Yahong 1 ; Yue, Tianli 1 ;

作者机构： 1.Northwest Univ, Coll Food Sci & Technol, Xian 710069, Peoples R China

2.China Elect Technol Grp Corp 20th Res Inst, Beijing 710068, Peoples R China

3.Chinese Acad Agr Sci, Inst Vegetables & Flowers, Beijing 100081, Peoples R China

关键词： Bongkrekic acid; HS-GC-IMS; Deep learning; Detection; Burkholderia gladioli pv. Cocovenenans

期刊名称：FOOD CHEMISTRY （ 影响因子：9.8； 五年影响因子：9.7 ）

ISSN： 0308-8146

年卷期： 2025 年 486 卷

页码：

收录情况： SCI

摘要： The shortage in early detection methods for the pathogen Burkholderia gladioli pv. cocovenenans (BGC) and its toxin bongkrekic acid rises the risk for food poisoning. Combining Headspace-Gas Chromatography-Ion Mobility Spectrometry (HS-GC-IMS) with convolutional neural network, we established a HS-GC-IMS-VGGNet architecture for the detection of BGC in edible fungus Auricularia auricula (AA) contaminated by six microorganisms species and the overall accuracy was 93.8 %. Meanwhile, the network achieved a limit of detection (LOD) of 80 CFU/mL and limit of quantification (LOQ) of 241 CFU/mL for BGC biomass. An LOD of 0.25 mg/L for bongkrekic acid detection covering the range of 0-1.54 mg/kg was achieved directly in the AA matrix. Furthermore, 28 microbial organic volatiles were extracted by Gradient-weighted Class Activation Mapping (Grad-CAM) and identified as conducive to the BKA detection. In all, the detection system established for BGC and its bongkrekic acid toxin is of good accuracy, precision and possesses greener mode.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：中国农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台