一种使用UV-Vis叠加光谱和深度学习的多元重金属检测方法
Hailong Zhang1, Qiannan Duan2, Lehan Sun1
1Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Xi'an Key Laboratory of Environmental Simulation and Ecological Health in the Yellow River Basin, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, PR China.
Journal of hazardous materials
|July 15, 2025
概括
这项研究介绍了一种使用UV-Vis光谱和深度学习来检测环境样本中的多重金属 (HM) 的AI驱动方法,克服光谱重叠的挑战,以有效监测.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 人工智能的人工智能
背景情况:
- 传统的重金属 (HM) 检测方法昂贵而复杂.
- 环境样本中的光谱重叠妨碍了准确的分析.
- 人工智能 (AI) 为智能光谱分析提供了潜力.
研究的目的:
- 开发一种用于同时多组件HM检测的新方法.
- 为了应对环境样本中的光谱重叠的挑战.
- 整合UV-Vis光谱与深度学习进行增强分析.
主要方法:
- 利用组合化学探针来提高色度测量反应的特异性.
- 收集高通量光谱数据用于模型训练.
- 采用了变压器深度学习模型进行端到端的光谱分析.
主要成果:
- 该模型在开发过程中实现了五个HM的高精度 (R2 = 0.936).
- 在现实世界样本中证明了可扩展性和稳定性,其中有十个HM (R2 = 0.681).
- 成功将光谱数据转化为定量生态风险概况.
结论:
- 由人工智能驱动的UV-Vis光谱方法能够准确,同时检测多个HM.
- 开发的AI软件自动检测HM和风险评估,实现快速环境监测.
- 为重金属污染控制提供主动的预警系统.
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