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疑似物筛选工作流程比较分析环境水样中的有机污染物。

Suspect screening workflow comparison for the analysis of organic xenobiotics in environmental water samples.

机构信息

Department of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain; Research Centre for Experimental Marine Biology and Biotechnology (PIE), University of the Basque Country (UPV/EHU), Plentzia, Basque Country, Spain.

Department of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain.

出版信息

Chemosphere. 2021 Jul;274:129964. doi: 10.1016/j.chemosphere.2021.129964. Epub 2021 Feb 15.

Abstract

Suspect screening techniques are able to determine a broader range of compounds than traditional target analysis. However, the performance of the suspect techniques relies on the procedures implemented for peak annotation and for this, the list of potential candidates is clearly a limiting factor. In order to study this effect on the number of compounds annotated in environmental water samples, a method was validated in terms of absolute recoveries, limits of quantification and identification, as well as the peak picking capability of the software (Compound Discoverer 2.1) using a target list of 178 xenobiotics. Four suspect screening workflows using different suspect lists were compared: (i) the Stoffident list, (ii) all the NORMAN lists, (iii) suspects containing C, H, O, N, S, P, F or Cl in their molecular formula with more than 10 references in Chemspider and (iv) the mzCloud library. The results were compared in terms of the number of annotated compounds at each confidence level. The same 8 compounds (atenolol, caffeine, caprolactam, carbendazim, cotinine, diclofenac, propyphenazone and trimetoprim) were annotated at the highest confidence level using the four workflows. Remarkable differences were observed for lower confidence levels but only 4 features were annotated at different levels by the four workflows. While the third approach provided the highest number of annotated features, the workflow based on the mzCloud library rendered satisfactory results with a simpler approach. Finally, this latter approach was extended to the analysis of organic xenobiotics in different environmental water samples.

摘要

可疑物筛查技术能够比传统的目标分析方法更广泛地检测到化合物。然而,可疑物筛查技术的性能依赖于峰注释所采用的程序,而对于可疑物筛查技术来说,潜在候选物的列表显然是一个限制因素。为了研究这对环境水样中注释化合物数量的影响,使用目标列表(178 种外来化合物),根据绝对回收率、定量下限和鉴定以及软件(Compound Discoverer 2.1)的峰拾取能力对方法进行了验证。比较了四种使用不同可疑物列表的可疑物筛查工作流程:(i)Stoffident 列表,(ii)所有 NORMAN 列表,(iii)分子公式中含有 C、H、O、N、S、P、F 或 Cl 且在 Chemspider 中有 10 个以上参考文献的可疑物,以及(iv)mzCloud 库。在每个置信度水平下,根据注释化合物的数量对结果进行了比较。在四种工作流程中,使用同样的 8 种化合物(阿替洛尔、咖啡因、己内酰胺、苯菌灵、可替宁、双氯芬酸、丙氧芬和甲氧苄啶)在最高置信度水平下进行了注释。在较低的置信度水平下观察到了显著的差异,但只有 4 种特征在 4 种工作流程中在不同水平上被注释。虽然第三种方法提供了最多数量的注释特征,但基于 mzCloud 库的工作流程通过一种更简单的方法取得了令人满意的结果。最后,将这种方法扩展到不同环境水样中的有机外来化合物分析中。

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