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开发并应用了一个内部文库和工作流程,用于环境样品的气相色谱-电子电离-精确质量/高分辨率质谱筛选。

Development and application of an in-house library and workflow for gas chromatography-electron ionization-accurate-mass/high-resolution mass spectrometry screening of environmental samples.

机构信息

Department of Analytical Chemistry, Institute of Research On Chemical and Biological Analysis (IAQBUS), Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain.

INTECMAR - Technological Institute for the Monitoring of the Marine Environment of Galicia, Peirao de Vilaxoán S/N, 36611, Vilagarcía de Arousa, Spain.

出版信息

Anal Bioanal Chem. 2022 Sep;414(21):6327-6340. doi: 10.1007/s00216-021-03810-w. Epub 2021 Dec 4.

DOI:10.1007/s00216-021-03810-w
PMID:34865195
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9372009/
Abstract

This work presents an optimized gas chromatography-electron ionization-high-resolution mass spectrometry (GC-EI-HRMS) screening method. Different method parameters affecting data processing with the Agilent Unknowns Analysis SureMass deconvolution software were optimized in order to achieve the best compromise between false positives and false negatives. To this end, an accurate-mass library of 26 model compounds was created. Then, five replicates of mussel extracts were spiked with a mixture of these 26 compounds at two concentration levels (10 and 100 ng/g dry weight in mussel, 50 and 500 ng/mL in extract) and injected in the GC-EI-HRMS system. The results of these experiments showed that accurate mass tolerance and pure weight factor (combination of reverse-forward library search) are the most critical factors. The validation of the developed method afforded screening detection limits in the 2.5-5 ng range for passive sampler extracts and 1-2 ng/g for mussel sample extracts, and limits of quantification in the 0.6-3.2 ng and 0.1-1.8 ng/g range, for the same type of samples, respectively, for 17 model analytes. Once the method was optimized, an accurate-mass HRMS library, containing retention indexes, with ca. 355 spectra of derivatized and non-derivatized compounds was generated. This library (freely available at https://doi.org/10.5281/zenodo.5647960 ), together with a modified Agilent Pesticides Library of over 800 compounds, was applied to the screening of passive samplers, both of polydimethylsiloxane and polar chemical integrative samplers (POCIS), and mussel samples collected in Galicia (NW Spain), where a total of 75 chemicals could be identified.

摘要

本工作提出了一种优化的气相色谱-电子电离-高分辨率质谱(GC-EI-HRMS)筛选方法。为了在假阳性和假阴性之间取得最佳折衷,优化了不同的方法参数,以影响 Agilent Unknowns Analysis SureMass 解卷积软件的数据处理。为此,创建了一个包含 26 种模型化合物的精确质量库。然后,将这些 26 种化合物的混合物以两个浓度水平(贻贝类干重 10 和 100ng/g,提取物 50 和 500ng/mL)加入 5 个贻贝类提取物的重复样本中,并注入 GC-EI-HRMS 系统。这些实验的结果表明,精确质量容限和纯权重因子(反向正向文库搜索的组合)是最关键的因素。所开发方法的验证结果表明,对于被动采样器提取物,筛选检测限在 2.5-5ng 范围内,对于贻贝类样品提取物,筛选检测限在 1-2ng/g 范围内,对于同一类型的样品,定量限分别在 0.6-3.2ng 和 0.1-1.8ng/g 范围内,用于 17 种模型分析物。方法优化后,生成了一个包含约 355 种衍生和非衍生化合物的精确质量 HRMS 库,其中包含保留指数。该库(可在 https://doi.org/10.5281/zenodo.5647960 免费获得),以及一个超过 800 种化合物的改良版 Agilent Pesticides Library,被应用于被动采样器(包括聚二甲基硅氧烷和极性化学整合采样器(POCIS))和在西班牙西北部加利西亚收集的贻贝类样本的筛选,总共可以识别出 75 种化学物质。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f40/9372009/abe4dbb074c4/216_2021_3810_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f40/9372009/9d50cb8c7c07/216_2021_3810_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f40/9372009/a5b210dded92/216_2021_3810_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f40/9372009/abe4dbb074c4/216_2021_3810_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f40/9372009/9d50cb8c7c07/216_2021_3810_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f40/9372009/a5b210dded92/216_2021_3810_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f40/9372009/abe4dbb074c4/216_2021_3810_Fig3_HTML.jpg

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