使用高斯混合模型的噪声过算法,用于自然有机物质的高分辨率质谱
Alexander A Potemkin1, Mikhail A Proskurnin1, Dmitry S Volkov1
1Chemistry Department of M.V. Lomonosov Moscow State University, Leninskie Gory, 1-3, GSP-1, Moscow 119991, Russia.
Analytical chemistry
|March 26, 2024
概括
一个新的高斯混合模型 (GMM) 算法有效地过了自然有机物质 (NOM) 质谱中的噪声. 这种自动化方法可以在不需要手动调整的情况下,提高分子公式分配的准确性高达15%.
科学领域:
- 分析化学 分析化学
- 环境科学 环境科学
- 机器学习应用 机器学习应用
背景情况:
- 自然有机物质 (NOM) 的高分辨率质谱经常被显著的噪声信号污染.
- 目前用于质谱的噪声过方法依赖于手动组图分析,导致变化和缺乏自动化.
- 噪声会干扰在非目标分析中精确的分子组成估计,因为噪声峰值可以被赋予经验公式.
研究的目的:
- 开发和评估一种新的,自动化的算法,用于NOM的质谱中的噪声过.
- 为了克服现有方法的局限性,特别是手动参数调整和基因图依赖.
- 评估新的噪声过方法对分子公式分配的准确性的影响.
主要方法:
- 基于高斯混合模型 (GMMs) 的噪声过算法的实施,这是一种机器学习技术.
- 基于GMM的过器自动从光谱数据直接确定强度值,消除了对直方图的依赖.
- 使用NOM质谱与不同数量的平均微扫描进行性能评估,与现有过器进行比较.
主要成果:
- 基于GMM的算法提供了一个数据驱动的,自动化的方法,用于在质谱中的噪声过.
- 该方法在没有用户干预或可定制参数的情况下成功识别和过噪声信号.
- 噪声过始终提高了正确分配的分子公式的速度,根据数据采集参数 (例如,平均显微扫描) 的不同,提高了高达15%.
结论:
- 提出的基于GMM的噪声过算法为质谱数据处理提供了强大的自动化解决方案.
- 这种方法通过提高分子配方识别率来提高非向分析的可靠性.
- 该算法的有效性证明了机器学习在推进复杂环境样本分析化学技术方面的潜力.
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