Matching unknown empirical formulas to chemical structure using LC/MS TOF accurate mass and database searching: example of unknown pesticides on tomato skins.
Matching unknown empirical formulas to chemical structure using LC/MS TOF accurate mass and database searching: example of unknown pesticides on tomato skins.
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DOI：
10.1016/j.chroma.2004.11.007
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发表时间：
2005-03
期刊：
影响因子：
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通讯作者：
E. Thurman;I. Ferrer;A. Fernández-Alba
中科院分区：
文献类型：
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作者：
E. Thurman;I. Ferrer;A. Fernández-Alba
传统上，食品中未知农药的筛选是采用传统的文库检索方法，通过GC/MS方法完成的。然而，许多新的极性和热不稳定的农药及其降解物更容易用LC/MS方法分析，目前还没有可搜索的库（除了一些用户库，这是有限的）。因此，需要采用LC/MS方法检测食品中未知的非目标农药。本报告开发了一种鉴定方案，结合使用LC/MS飞行时间（精确质量）和LC/MS离子阱质谱（MS/MS），并搜索通过精确质量和ChemIndex数据库或Merck Index数据库生成的经验公式。该方法与传统的片段离子文库检索方法不同。这里的概念由四个部分组成。首先是使用精确的质量和生成经验公式，对实际市场上的蔬菜提取物（番茄皮）中可能存在的未知农药进行初步检测。第二种方法是在Merck Index数据库的CD（10,000种化合物）或ChemIndex（77,000种化合物）上搜索可能的结构。三是对番茄皮提取物中未知农药进行质谱分析，利用化学制图软件进行片段离子鉴定，并与精确质量的离子片段进行比对。第四是用真实的标准进行验证，如果有的话。三个未知的，非目标杀虫剂的例子，使用从实际市场样品中提取的番茄皮提取物。讨论了该方法的局限性，包括使用A+2同位素特征，扩展数据库，缺乏真实的标准，以及食品提取物中的天然产物未知。
Traditionally, the screening of unknown pesticides in food has been accomplished by GC/MS methods using conventional library searching routines. However, many of the new polar and thermally labile pesticides and their degradates are more readily and easily analyzed by LC/MS methods and no searchable libraries currently exist (with the exception of some user libraries, which are limited). Therefore, there is a need for LC/MS approaches to detect unknown non-target pesticides in food. This report develops an identification scheme using a combination of LC/MS time-of-flight (accurate mass) and LC/MS ion trap MS (MS/MS) with searching of empirical formulas generated through accurate mass and a ChemIndex database or Merck Index database. The approach is different than conventional library searching of fragment ions. The concept here consists of four parts. First is the initial detection of a possible unknown pesticide in actual market-place vegetable extracts (tomato skins) using accurate mass and generating empirical formulas. Second is searching either the Merck Index database on CD (10,000 compounds) or the ChemIndex (77,000 compounds) for possible structures. Third is MS/MS of the unknown pesticide in the tomato-skin extract followed by fragment ion identification using chemical drawing software and comparison with accurate-mass ion fragments. Fourth is the verification with authentic standards, if available. Three examples of unknown, non-target pesticides are shown using a tomato-skin extract from an actual market place sample. Limitations of the approach are discussed including the use of A+2 isotope signatures, extended databases, lack of authentic standards, and natural product unknowns in food extracts.