数字农科院2.0

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： Chen Niu;Mincheng Zhao;Qinglin Sheng;Yuanchun Wu;Zihan Song;Jianping Wei;Yahong Yuan;Tianli Yue

作者机构：

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

期刊名称： Food Chemistry

ISSN： 0308-8146

年卷期： 2025 年 486 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

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.

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