一种新的光谱深度学习方法用于水性多重金属检测
Zhizhi Fu1, Qianru Wan1, Qiannan Duan2
1Laboratory of Environmental Aquatic Chemistry, Department of Environmental Science, Shaanxi Normal University, Xi'an, 710062, P. R. China. jianchaolee@snnu.edu.cn.
Analytical methods : advancing methods and applications
|January 8, 2025
概括
这项研究引入了一种新的数字光谱成像系统和深度学习模型,用于快速检测水中的重金属. 该ResNet-50模型准确预测,和铜度,提供一个高效的环境监测解决方案.
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:
- 水中的重金属污染对环境构成重大风险.
- 传统的检测方法往往耗时且昂贵.
- 人工智能 (AI),包括机器学习 (ML) 和深度学习 (DL),对分析化学有希望,但需要大量的光谱数据.
研究的目的:
- 开发一种高效,快速的方法来检测混合水样中的重金属度.
- 克服AI模型培训传统方法的数据采集局限性.
- 利用深层卷积神经网络,同时检测多种重金属.
主要方法:
- 开发了一种新的数字光谱成像系统,以快速收集光谱数据.
- 从混合重金属样本中获得了3000个数字光谱.
- 进行端到端深卷积神经网络回归模型 (ResNet-50,Inception V1,SqueezeNet V1.1) 的训练,以预测重金属度.
主要成果:
- ResNet-50模型在同时检测,和铜方面表现出高精度.
- 在真实和预测重金属值之间实现了超过0.99的线性合适系数.
- 开发的系统可以在复杂的水表中快速有效地检测重金属.
结论:
- 该研究提出了一种有效的AI驱动的方法,用于快速检测环境水样中的重金属.
- 这种方法克服了传统技术在AI速度和数据要求方面的局限性.
- 这些发现作为一个有价值的参考,用于推进环境监测中的智能分析技术.
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