使用机器学习预测环境相关化合物的双重质谱的诊断信息
S Codrean1, B Kruit1, N Meekel2
1Faculty of Science, Artificial Intelligence, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.
Analytical chemistry
|October 9, 2023
概括
一个新的机器学习模型自动评估环境化合物的双重质谱 (MS2) 的质量. 该工具可以区分良好的诊断信息和不良的诊断信息,从而提高各种应用程序的数据可靠性.
科学领域:
- 环境化学环境化学
- 分析化学 分析化学
- 计算化学的计算化学
背景情况:
- 高质量的双重质谱 (MS2) 对于识别环境化合物和预测其特性至关重要.
- 目前的方法缺乏对环境污染物的MS2光谱进行自动化质量评估.
- 电子喷雾电离通常用于分析这些化合物.
研究的目的:
- 开发一种机器学习 (ML) 方法,用于自动评估MS2频谱的诊断质量.
- 具体解决通过电喷气电离分析的环境相关化合物的需求.
- 为下游应用提高MS2数据的可靠性.
主要方法:
- 训练了一个随机的森林分类器,使用来自204种环境污染物的大约1400个MS2光谱.
- 用液态色谱学与高分辨率质谱学相结合,采用不同碰撞能量来获得光谱.
- 进行了光谱质量的手动标记,随后进行了特征工程和ML模型输入的选择.
主要成果:
- 该ML模型在区分好坏的MS2光谱方面实现了84%的准确性,88%的精度和75%的回忆.
- 选择的特征有效地代表了诊断信息 (碎片数,强度,范围,分子离子,噪声).
- 该模型在分类MS2光谱质量方面表现强.
结论:
- 开发的ML模型可靠地区分环境化合物的高和低诊断质量的MS2光谱.
- 这种自动化质量评估有可能显著改善依赖MS2数据的应用程序.
- 该方法为环境污染物的分析和风险评估提供了有价值的工具.
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