Chemistry Department, National Food Agency, Box 622, 751 26, Uppsala, Sweden.
Department of Environmental Science and Analytical Chemistry, Stockholm University, Svante Arrhenius väg 8, 114 18, Stockholm, Sweden.
Anal Bioanal Chem. 2018 Sep;410(22):5593-5602. doi: 10.1007/s00216-018-1028-4. Epub 2018 Mar 29.
A non-target analysis method for unexpected contaminants in food is described. Many current methods referred to as "non-target" are capable of detecting hundreds or even thousands of contaminants. However, they will typically still miss all other possible contaminants. Instead, a metabolomics approach might be used to obtain "true non-target" analysis. In the present work, such a method was optimized for improved detection capability at low concentrations. The method was evaluated using 19 chemically diverse model compounds spiked into milk samples to mimic unknown contamination. Other milk samples were used as reference samples. All samples were analyzed with UHPLC-TOF-MS (ultra-high-performance liquid chromatography time-of-flight mass spectrometry), using reversed-phase chromatography and electrospray ionization in positive mode. Data evaluation was performed by the software TracMass 2. No target lists of specific compounds were used to search for the contaminants. Instead, the software was used to sort out all features only occurring in the spiked sample data, i.e., the workflow resembled a metabolomics approach. Procedures for chemical identification of peaks were outside the scope of the study. Method, study design, and settings in the software were optimized to minimize manual evaluation and faulty or irrelevant hits and to maximize hit rate of the spiked compounds. A practical detection limit was established at 25 μg/kg. At this concentration, most compounds (17 out of 19) were detected as intact precursor ions, as fragments or as adducts. Only 2 irrelevant hits, probably natural compounds, were obtained. Limitations and possible practical use of the approach are discussed.
描述了一种用于检测食品中意外污染物的非靶向分析方法。许多当前被称为“非靶向”的方法能够检测数百甚至数千种污染物。然而，它们通常仍然会错过所有其他可能的污染物。相反，代谢组学方法可能用于获得“真正的非靶向”分析。在本工作中，优化了该方法以提高低浓度下的检测能力。该方法使用 19 种化学性质不同的模型化合物（模拟未知污染）掺入牛奶样品中进行评估。其他牛奶样品用作参考样品。所有样品均采用 UHPLC-TOF-MS（超高效液相色谱-飞行时间质谱）进行分析，采用反相色谱和正离子模式电喷雾电离。数据评估由 TracMass 2 软件执行。未使用特定化合物的目标列表来搜索污染物。相反，该软件用于整理仅出现在加标样品数据中的所有特征，即该工作流程类似于代谢组学方法。峰的化学鉴定程序不在研究范围内。优化了方法、研究设计和软件设置，以最大程度地减少手动评估、错误或不相关的命中，并最大限度地提高加标化合物的命中率。建立了 25μg/kg 的实用检测限。在该浓度下，大多数化合物（19 种中的 17 种）作为完整的前体离子、碎片或加合物被检测到。仅获得 2 个不相关的命中，可能是天然化合物。讨论了该方法的局限性和可能的实际用途。
