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深度PFAS:通过深度学习实现 PFAS 的快速注释:通过光谱编码和潜在空间分析增强非目标选.

Heng Wang1, Tien-Chueh Kuo2, Yufeng Jane Tseng1,2,3,4

  • 1Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.

Environmental science & technology
|September 30, 2025
PubMed
概括

检测和多醇基物质 (PFAS) 是很困难的. 一种新的深度学习方法,DeePFAS,使用MS2光谱快速注释PFAS,简化环境分析.

关键词:
PFAS (多-和多基基物质) 的使用.化学潜伏的空间是化学潜伏的空间.深度学习是一种深度学习.环境分析环境分析质谱测量质谱测量质谱测量质谱测量质量测量质谱测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量

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科学领域:

  • 环境化学环境化学
  • 分析化学 分析化学
  • 人工智能的人工智能

背景情况:

  • 由于化学多样性,复杂的矩阵和微量水平,检测Per-和多基物质 (PFAS) 是一个挑战.
  • 像LC-HRMS这样的现有方法面临着污染,劳动力,检测限制和数据处理等问题.
  • 一种通用PFAS检测方法受到背景污染和大量化合物的阻碍.

研究的目的:

  • 在复杂样本中开发一种快速有效的PFAS注释方法.
  • 克服目前用于PFAS分析的LC-HRMS技术的局限性.
  • 利用人工智能来简化大规模的非目标PFAS查.

主要方法:

  • 开发了DeepPFAS,这是一种用于PFAS注释的深度学习方法.
  • 采用了带有卷积和变压器架构的光谱编码器.
  • 将原始的MS2光谱投射到化学结构特征的潜在空间中.

主要成果:

  • 通过将潜伏表征与候选分子进行比较,DeePFAS使MS2光谱的有效注释成为可能.
  • 该方法简化了大规模的非目标PFAS查.
  • 减少了PFAS检测中的分析复杂性.

结论:

  • 迪帕斯 (DeePFAS) 展示了人工智能在环境化学中的潜力,用于PFAS分析.
  • 深度学习方法为PFAS识别提供了更快,更有效的替代方案.
  • 这种方法可以帮助克服目前在PFAS检测和查方面的障碍.