质谱法
色谱法
残留物(化学)
高分辨率
化学
杀虫剂
液相色谱-质谱法
农药残留
生物
有机化学
遥感
农学
地质学
作者
Huiqin Pan,Guyu Zhao,Ying Tan,Miao Shui,Xiuhong Mao,Shen Ji,Heng Zhou,Qing Hu
标识
DOI:10.1080/10408347.2025.2511140
摘要
The safety evaluation of herbal medicines faces a critical challenge due to exogenous pesticide residues, where traditional detection methods struggle to address the dual complexity of highly variable phytochemical matrices and trace-level multi-residue contaminants. Recent regulatory shifts emphasizing large-scale detection have positioned liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) as a transformative solution. This review systematically examines cutting-edge LC-HRMS workflows tailored for complex herbal matrices, with three pivotal technical dimensions: (1) Matrix-specific optimization spanning from QuEChERS-based sample preparation to chromatographic separation protocols that reduce matrix interferences; (2) Intelligent data acquisition strategies balancing selectivity and coverage through adaptive MS/MS triggering and narrow-window fragmentation; (3) Integrated analytical frameworks combining targeted screening with expanding pesticide databases and non-targeted approaches leveraging retrospective HRMS data mining. While LC-HRMS has demonstrated exceptional performance in food safety domains, its application in herbal medicine analysis remains constrained by insufficient method harmonization and underutilized data potential. We critically evaluate how emerging techniques, including comprehensive two-dimensional liquid chromatography, ion mobility mass spectrometry, structure-informed parameter prediction, suspect screening based on biotransformation, and metabolomics-driven non-targeted screening, could overcome current limitations in compound identification confidence and pesticide coverage. By bridging technological advancements with the challenges faced in practical residue analysis of herbal medicines, this review provides actionable guidelines to empower researchers in developing robust, future-proof analytical schemes that meet evolving regulatory standards and public health expectations.
科研通智能强力驱动
Strongly Powered by AbleSci AI