使用自动化目标选 (ATS) 工作流程处理有问题的离子色谱,用于无监督分析高分辨率质谱数据
Georg Braun1, Martin Krauss2, Stephanie Spahr3
1Department of Cell Toxicology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany. Georg.braun@ufz.de.
Analytical and bioanalytical chemistry
|March 31, 2024
概括
一个新的基于R的工作流自动化环境和生物样本的分析,使用液体染色学 (LC) 和气体染色学 (GC) 与高分辨率质谱学 (HRMS) 相结合. 这种自动化目标选 (ATS) 提高了数据评估的准确性和效率,无需人工干预.
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:
- 液体染色学 (LC) 和气体染色学 (GC) 与高分辨率质谱学 (HRMS) 相结合,对于分析环境和生物样本中的污染物至关重要.
- 现有的数据分析工具难以处理复杂的数据集,缺乏针对性选的全自动化工作流程.
- 挑战包括杂的染色体图,保留时间转移和复杂的峰值模式.
研究的目的:
- 开发和呈现一个完全不受监督的,基于R的工作流程,用于HRMS数据的自动化目标选 (ATS).
- 为了使高效和准确的峰值注释和量化从GC-EI和LC-ESI (负和正模式) 的HRMS数据.
- 解决当前用于HRMS数据评估的半自动化和供应商特定软件的局限性.
主要方法:
- 开发基于R的工作流程,用于自动化峰值注释和量化.
- 将工作流应用于从地表水提取物中获得的Orbitrap HRMS数据.
- 对比ATS工作流与供应商软件 (TraceFinder) 和MZmine进行数据评估.
主要成果:
- 在ATS工作流中,表现出更好的峰值注释精度:0.86 (LC-ESIpos),0.83 (LC-ESIneg) 和0.76 (GC-EI),表现优于MZmine.
- 与手动量化相比,ATS量化的平均百分比误差约为30%.
- 工作流成功地处理了具有挑战性的数据,包括杂的染色体图和保留时间转移,没有后期纠正.
结论:
- 开发的基于R的ATS工作流提供了一种节省时间和自动化解决方案,用于分析GC和LC-HRMS数据.
- ATS显著加快并提高了针对性查对大量分析物和样本的大规模研究的适用性.
- 工作流的无监督性减少了手动干预的需要,提高了整体分析吞吐量和可靠性.
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