在没有标准参考材料的情况下,向机器学习驱动的三合体的质谱识别
Brian P Mayer1, Mark L Dreyer1, Maria C Prieto Conaway1
1Forensic Science Center, Lawrence Livermore National Laboratory, 7000 East Avenue L-090, Livermore, California 94550, United States.
Analytical chemistry
|August 22, 2023
概括
机器学习可以在质谱数据中识别独特的碎片,以检测未知的三甲毒素. 这种新方法有助于法医和公共卫生,通过识别以前无法检测到的改性真菌毒素.
科学领域:
- 分析化学 分析化学
- 质谱测量质量谱测量
- 毒理学 毒理学 毒理学
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 由于缺乏分析标准和光谱数据库,现有的方法难以识别未经表征和未知修饰的三甲毒素.
- 这种识别差距给法医分析和公共卫生带来了重大挑战.
- 修改后的三甲基可从生物,环境和其他转化过程中产生的.
研究的目的:
- 利用机器学习开发一种新的方法来识别未知,结构相似的三合体场.
- 利用机器学习从质谱数据中识别歧视性碎片离子.
- 建立一种新的方法来检测A型和B型三甲,包括未知的变体.
主要方法:
- 机器学习 (ML) 技术的应用,特别是渐变增强的机器学习器,以识别歧视性碎片离子.
- 在三重四极质谱仪 (QQQ) 上开发ML驱动的前体离子扫描 (PIS) 方法.
- 使用开发的PIS方法选"未知未知"三甲基.
主要成果:
- 通过使用ML成功识别了标志着A型和B型三合甲烯的歧视性碎片离子.
- 开发并应用ML驱动的PIS方法,以提高选能力.
- 通过对合成化合物的初步测试,证明了ML驱动的质谱仪在检测新型三甲烯变体方面的潜力.
结论:
- 机器学习提供了一种强大的方法来克服识别未知的三二甲毒素的局限性.
- 开发的基于机器学习的PIS方法为法医和公共卫生应用提供了一个有前途的工具.
- 这项研究代表了利用客观ML技术对菌毒素进行先进的质谱分析的重要一步.
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