环境样本中CEC的定量非目标分析可以通过考虑所有质量附加物来改进
Selina Tisler1, Kristoffer Kilpinen1,2, David I Pattison1
1Analytical Chemistry Group, Department of Plant and Environmental Science, Faculty of Science, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark.
Analytical chemistry
|December 21, 2023
概括
这项研究通过包括所有质量引证来增强定量非目标分析 (qNTA),显著提高了环境污染物的预测准确性. 这种先进的方法为新兴关注污染物 (CEC) 提供了更可靠的度预测.
科学领域:
- 环境化学环境化学
- 分析化学 分析化学
- 质谱测量质量谱测量
背景情况:
- 使用液体染色学-高分辨率质谱学 (LC-HRMS) 的定量非目标分析 (qNTA) 提供了全面的环境样本评估.
- 以前的模型将电离效率与分子描述符相关联,以预测度,达到5的误差因子.
研究的目的:
- 通过将所有观察到的质量添加物纳入预测模型来提高qNTA的准确性和可靠性.
- 为预测污染物度和方法可转移性开发一个强大的定量结构-属性关系 (QSPR) 模型.
主要方法:
- 开发了一个QSPR模型,包括所有质 adducts,而不仅仅是质子离子,为216新兴关注污染物 (CECs).
- 构建了一个二级QSPR模型来预测可转移性因子,并考虑依赖序列的响应因子变化.
- 使用分析标准和废水废水样本验证了该模型,这些样本在两年间隔和第二台仪器上进行了分析.
主要成果:
- 包括所有质量添加物,测试组确定系数 (Q2) 从0.670提高到0.855 (中位射频误差系数1.6).
- 该模型表现出良好的可转移性,标准的预测折变的中位数为1.74,废水样本的预测折变为2.4.
- 80%的化合物预测在2.4 (标准) 和3.3 (废水) 的折叠变化范围内,其中80%的26种废水化合物预测在第二个仪器上的3.8因子内.
结论:
- 将所有质量 adducts 纳入 qNTA 显著提高了环境监测的准确性.
- 开发的QSPR模型提供了可靠的污染物度预测和方法在不同时间点和仪器的可转移性.
- 这种精细的qNTA方法为评估CECs环境污染提供了更全面,更准确的工具.
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