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使用边界回归模型评估和降低气相色谱 - 质谱联用分析中的分析不确定性

Evaluation and reduction of the analytical uncertainties in GC-MS analysis using a boundary regression model.

作者信息

Yu Miao, Hou Xingwang, Liu Qian, Wang Yawei, Liu Jiyan, Jiang Guibin

机构信息

State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.

State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.

出版信息

Talanta. 2017 Mar 1;164:141-147. doi: 10.1016/j.talanta.2016.11.046. Epub 2016 Nov 21.

DOI:10.1016/j.talanta.2016.11.046
PMID:28107908
Abstract

The uncertainties in analysis of trace environmental pollutants may come from sample matrix and sample pretreatment process. In this study, a boundary model was developed based on the visualized data of mass spectrometry to evaluate the influences from sample matrix and pretreatment process. The factors affecting the pretreatment procedures, such as the solvents, the extraction sorbents and the extraction process had limited influences compared with matrix effects. Using such boundary model, we found that selecting suitable qualitative and quantitative ions for MS detector is more important for reducing the matrix effect in GC-MS analysis than the traditionally used methods of optimizing the pretreatment process since some clean up sorbents might be useless to reduce the matrix effects. As for 2,2',4,4',5-pentabromodiphenyl ether (BDE-99), the fragmental ions were usually used for qualitative and quantitative analysis, which however was easily affected by the matrix effects. While, molecular ions would eliminate the influences from the sample matrix. Such a model could be used to decrease the uncertainty and increase the accuracy of environmental trace analysis.

摘要

痕量环境污染物分析中的不确定性可能源于样品基质和样品预处理过程。在本研究中,基于质谱可视化数据开发了一种边界模型,以评估样品基质和预处理过程的影响。与基质效应相比,影响预处理程序的因素,如溶剂、萃取吸附剂和萃取过程,影响有限。使用这种边界模型,我们发现,对于气相色谱 - 质谱分析中减少基质效应而言,为质谱检测器选择合适的定性和定量离子比传统的优化预处理过程的方法更为重要,因为一些净化吸附剂可能对减少基质效应无效。对于2,2',4,4',5 - 五溴二苯醚(BDE - 99),碎片离子通常用于定性和定量分析,但很容易受到基质效应的影响。而分子离子则可以消除样品基质的影响。这样的模型可用于降低环境痕量分析的不确定性并提高其准确性。

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