Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Division of Metrology in Chemistry, National Institute of Metrology, Beijing 100029, China.
State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China.
Molecules. 2022 Aug 17;27(16):5251. doi: 10.3390/molecules27165251.
Dinotefuran (DNT) is a neonicotinoid insecticide widely used in pest control. Identification of structurally related impurities is indispensable during material purification and pesticide registration and certified reference material development, and therefore needs to be carefully characterized. In this study, a combined strategy with liquid chromatography high-resolution mass spectrometry and SIRIUS has been developed to elucidate impurities from DNT material. MS and MS/MS spectra were used to score the impurity candidates by isotope score and fragment tree in the computer assisted tool, SIRIUS. DNT, the main component, worked as an anchor for formula identification and impurity structure elucidation. With this strategy, two by-product impurities and one stereoisomer were identified. Their fragmentation pathways were concluded, and the mechanism for impurity formation was also proposed. This result showed a successful application for combined human intelligence and machine learning, in the identification of pesticide impurities.
二嗪磷(DNT)是一种广泛用于害虫防治的新烟碱类杀虫剂。在物质纯化、农药登记和标准物质开发过程中,识别结构相关的杂质是必不可少的,因此需要仔细加以表征。在这项研究中,采用液相色谱高分辨质谱和 Sirius 相结合的策略,阐明了 DNT 材料中的杂质。通过计算机辅助工具 Sirius 中的同位素得分和碎片树,使用 MS 和 MS/MS 谱对杂质候选物进行评分。主成分 DNT 作为公式鉴定和杂质结构阐明的锚点。采用该策略,鉴定出两种副产物杂质和一种立体异构体。总结了它们的裂解途径,并提出了杂质形成的机理。该结果表明,在农药杂质的鉴定中,成功地应用了人机结合和机器学习。