在质谱学中用于非目标选的特征检测算法参数优化的随机方法
Mohammad Sadia1, Youssef Boudguiyer2, Rick Helmus2
1Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands. m.s.sadia@uva.nl.
Analytical and bioanalytical chemistry
|July 12, 2024
概括
使用随机方法优化非目标选 (NTS) 中的特征检测参数,可以减少误报. 这种方法通过提高算法性能,改善了水样中的和多基物质 (PFAS) 检测.
科学领域:
- 环境化学环境化学
- 分析化学 分析化学
- 计算化学计算化学
背景情况:
- 非目标选 (NTS) 对于识别复杂矩阵中的未知化合物至关重要.
- 高分辨率质谱法 (HRMS) 中的精确特征检测受到假阳性 (FP) 的挑战.
- 优化算法参数对于最小化FP和提高NTS可靠性至关重要.
研究的目的:
- 开发和验证一种随机方法来优化NTS中的特征检测算法参数.
- 为了提高HRMS数据处理的准确性和降低HRMS数据处理的复杂性,用于对per-和多醇基物质 (PFAS) 的分析.
- 评估参数优化对FP减少和真正阳性检测的影响.
主要方法:
- 使用随机抽样和皮尔森相关性的随机策略来优化参数.
- 在patRoon平台上评估了四个开源算法 (OpenMS,SAFD,XCMS,KPIC2).
- 该方法应用于含有46个PFAS的饮用水样本,并对其他样本进行验证.
主要成果:
- 优化的参数改善了算法性能,增加了SAFD和XCMS的可疑命中.
- 优化减少了OpenMS.的检测特征 (FP) 的总数.
- 经验证的结果显示,虚假发现率 (FDR%) 较低,真实阳性检测率增加.
结论:
- 通过随机方法优化参数对于有效的NTS至关重要.
- 这种方法提高了PFAS和其他污染物的HRMS数据分析的可靠性.
- 通过优化参数来减少FP,简化了数据解释,提高了分析效率.
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