高分辨质谱结合分子网络技术快速筛查小麦中农药及其代谢物

Rapid screening of pesticide and their metabolites in Triticum aestivum L. by high-resolution mass spectrometry combined with molecular network technology

中文关键词:  小麦  农残  代谢物  高分辨质谱  非靶向筛查  分子网络

英文关键词:Triticum aestivum L.  pesticide residues  metabolites  high-resolution mass spectrometry  non-targeted screening  molecular network

中文摘要:

      目的 建立超高效液相色谱-四极杆/静电场轨道阱高分辨质谱技术(ultra performance liquid chromatography coupled to quadrupole-orbitrap high-resolution mass spectrometry, UPLC-Q-Orbitrap HRMS)与分子网络策略的非靶向筛查方法, 用于小麦中农药残留及其代谢物的快速识别。方法 以加标小麦样品为对象, 利用UPLC-Q-Orbitrap HRMS采集高分辨质谱数据。原始数据经MSConvert格式转换后, 导入MZmine软件进行预处理, 再上传至全球天然产物社会分子网络平台(global natural products social molecular networking, GNPS)构建可视化分子网络。系统考察了MS2质谱图去噪阈值和GNPS网络构建参数(最小匹配碎片离子数、谱库搜索最小匹配数)对种子节点识别效率的影响, 并综合运用精确质量数、保留时间及特征碎片离子裂解规律对目标物进行鉴定。结果 当MS2去噪阈值设为1.0E3、GNPS最小匹配碎片离子数和谱库搜索最小匹配数均设置为2时, 分子网络注释率和整体种子节点识别率达到最优, 分别为58.9%和94.9%。在优化条件下, 通过分子网络成功聚类并识别了结构相近的农药, 以及杀线威、抗蚜威、咪鲜胺、亚胺硫磷、马拉硫磷的代谢产物, 并阐明了其主要裂解途径。结论 本研究建立的UPLC-Q-Orbitrap HRMS-分子网络整合分析策略, 能够实现小麦中结构相似农药及其代谢物的快速、高效筛查, 为复杂农产品基质中非靶向农药残留分析提供了可靠的技术方案和新的研究思路。

英文摘要:

      Objective To establish an untargeted screening method for rapidly identifying pesticide residues and their metabolites in Triticum aestivum L. by ultra performance liquid chromatography coupled to quadrupole-orbitrap high-resolution mass spectrometry (UPLC-Q-Orbitrap HRMS) combined with a molecular networking strategy. Methods Fortified Triticum aestivum L. samples were analyzed using UPLC-Q-Orbitrap HRMS to acquire high-resolution mass spectrometry data. The raw data were converted by MSConvert, imported into MZmine software for preprocessing, and then uploaded to the global natural products social molecular networking (GNPS) platform for constructing a visual molecular network. The effects of the MS2 spectral denoising threshold and GNPS network construction parameters (minimum matched fragment ions, minimum matched spectra for library search) on seed node identification efficiency were systematically investigated. Target compounds were identified by integrating accurate mass, retention time and characteristic fragment ion cleavage patterns. Results Optimal network annotation rate (58.9%) and seed node recognition rate (94.9%) were achieved when the MS2 denoising parameter was set to 1.0E3, and both the minimum matched fragment ions and library search minimum matches in GNPS were set to 2. Under the optimized conditions, structurally similar pesticides were successfully clustered and identified through molecular networking, along with the metabolites of oxamyl, pirimicarb, prochloraz, phosmet and malathion, with their major fragmentation pathways elucidated. Conclusion A UPLC-Q-Orbitrap HRMS-molecular networking non-targeted screening strategy is developed, enabling rapid and efficient identification of structurally similar pesticide residues and metabolites in Triticum aestivum L.. This approach provides a reliable analytical framework and technical methodology for screening non-targeted pesticide residues and metabolites in complex matrices.