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SR-Unet:基于深度神经网络的离子陷质谱仪的超高分辨率算法
Jiawen Ai1,2, Weize Zhao1,2, Quan Yu1
1Division of Advanced Manufacturing, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China.
Analytical chemistry
|November 14, 2023
概括
一个新的超分辨率U-net算法 (SR-Unet) 提高了微型质谱仪的性能. 这种人工智能方法提高了对场分析的离子识别和分辨率,使复杂混合物的检测更准确.
科学领域:
- 分析化学 分析化学
- 频谱测量是一种光谱测量.
- 科学中的人工智能.
背景情况:
- 质谱仪对于化学分析至关重要,微型版本可用于现场应用.
- 四极离子陷提供了小型化潜力,但通常分辨率低.
- 平衡质量灵敏度,分辨率和扫描速度是离子陷质谱仪的关键.
研究的目的:
- 开发用于离子陷质谱的超分辨率算法 (SR-Unet).
- 为了提高微型质谱仪的同等分辨率和检测能力,而不会牺牲速度或灵敏度.
主要方法:
- 实施超分辨率U-net算法 (SR-Unet) 来估计来自低分辨率光谱的离子.
- 在Turbo和正常扫描模式下使用线性离子陷质谱仪 (LTQ XL) 获取质谱.
- 将SR-Unet应用于用于检测挥发性有机化合物 (VOC) 和分析的微型质谱仪的数据.
主要成果:
- 实现了125,000 Da/s的单位质量分辨率,与16667 Da/s的分辨率相匹配.
- 在光化学评估监测站 (PAMS) 加强了VOC物种的识别,从31到50.
- 提高了检测的分辨率,将布拉迪基宁二价离子的FWHM从0.35降低到0.15Da (相当于3540分辨率) 在375Da/s.
结论:
- SR-Unet算法显著提高了离子陷质谱仪的分辨率和分析速度.
- 证明了SR-Unet的成功迁移和应用到微型质谱仪进行现场分析.
- 该方法提供了一种新的方法来提高便携式质谱仪器的混合物检测能力.
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