累积中性损失模型用于碎片解卷在电喷离子化,高分辨率质谱数据中的碎片解卷
Denice van Herwerden1, Jake W O'Brien1,2, Sascha Lege3
1Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam 1012 WX, The Netherlands.
Analytical chemistry
|August 7, 2023
概括
本研究引入了一种用于非目标分析 (NTA) 的光谱解卷的新算法,该算法使用了时间和质量领域. 累积中性损失 (CNL) 模型显著提高了在质谱中识别分子碎片的准确性.
科学领域:
- 分析化学 分析化学
- 质谱测量质量谱测量
- 计算化学计算化学
背景情况:
- 高分辨率质谱 (HRMS) 对于非目标分析 (NTA) 中的结构阐明至关重要.
- 现有的光谱清理工具主要利用时间域信息,限制了它们的有效性.
- 数据独立获取和直接输注实验往往产生复杂的光谱,需要强大的解卷.
研究的目的:
- 开发和验证用于光谱解卷的新型算法,该算法集成了时间和质域数据.
- 为了提高HRMS数据中碎片解卷的准确性和效率.
- 增强NTA工作流程中的结构阐明能力.
主要方法:
- 开发基于概率的累积中性损失 (CNL) 模型,用于断片解卷.
- 在算法中整合时间域和质域信息.
- 在0.005Da的质量公差和0.00的得分值的CNL模型的优化.
- 与保留时间差和峰值形状相关性方法进行比较.
主要成果:
- 优化的CNL模型实现了95.0%的真实阳性率 (TPr),错误发现率 (FDr) 为20.6%,减少率为35.4%.
- 在真实样本 (杀虫剂) 上的测试表明,在不同度和矩阵效应中,TPr高于88.8%.
- 与相关性分析和CNL模型相结合,产生了最高的有效性,TPR为84.7%,FDr为54.4%,减少率为51.0%.
结论:
- 开发的CNL模型为NTA的光谱解卷提供了显著的进步.
- 结合时间和质域信息,与仅使用时间域的方法相比,提供了更高的性能.
- CNL模型,特别是与相关性分析相结合时,是准确的断片解卷和结构阐释的强大工具.
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