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.

中文翻译：

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使用LC-HRMS对食品意外污染物进行非目标分析

描述了一种用于食品中意外污染物的非目标分析方法。当前许多被称为“非目标”的方法能够检测数百甚至数千种污染物。但是，它们通常仍会遗漏所有其他可能的污染物。取而代之的是，可以使用代谢组学方法来获得“真正的非目标”分析。在目前的工作中，对这种方法进行了优化，以提高低浓度下的检测能力。使用加标到牛奶样品中的19种化学上不同的模型化合物对这种方法进行了评估，以模拟未知污染。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），反相色谱和电喷雾电离以正模式进行分析。数据评估由软件TracMass 2进行。没有使用特定化合物的目标列表来搜索污染物。取而代之的是，该软件被用来分类仅出现在加标样品数据中的所有特征，即，工作流程类似于代谢组学方法。化学鉴定峰的程序不在研究范围内。优化了软件中的方法，研究设计和设置，以最大程度地减少手动评估和错误或不相关的命中率，并最大程度提高加标化合物的命中率。实际检测极限设定为25μg/ kg。在此浓度下，大多数化合物（19种化合物中的17种）被检测为完整的前体离子，碎片或加合物。仅获得了2个不相关的匹配，可能是天然化合物。