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一种2D数字光谱传感方法,用于快速检测水中的抗生素

Hailong Zhang¹, Pengwei Yan¹, Qiannan Duan²

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, China.

Environmental research

|November 7, 2025

概括

一种新方法使用光谱成像和深度学习来快速检测水中的抗生素. 这种方法为环境监测提供了低成本,高通量解决方案,结果仅需3分钟.

科学领域:

环境科学环境科学
分析化学分析化学
频谱学是一种光谱学.

背景情况:

抗生素是全球重要的污染物,需要有效检测生态和健康风险管理.
传统的抗生素检测方法昂贵且耗时,无法满足快速监测的需求.

研究的目的:

开发一种新,快速,经济高效的方法来检测水样中的抗生素.
将光谱成像与深度学习相结合,以加强抗生素分析.

主要方法:

利用光谱成像系统和化学探针生成二维数字光谱图像 (2D-DS图像).
开发了一个数字光谱卷积神经网络 (DSCNN) 模型,用于从2D-DS图像中对抗生素度进行端到端的定量分析.
应用第四个研究范式,由光谱大数据驱动,分析复杂的样本.

主要成果:

DSCNN模型实现了高预测精度,R2值在0.85到0.93.9之间.
实现了抗生素的1.94毫克L-1的低检测极限.
开发了一个快速检测平台,单次分析时间约为3分钟.

结论:

开发的方法为快速检测水中的抗生素提供了高吞吐量,低成本的解决方案.
这种方法为智能环境监测网络提供了坚实的技术基础.
频谱成像和深度学习的整合使复杂的环境样本能够有效分析.

关键词:

抗生素检测检测抗生素检测深度学习是一种深度学习.数字频谱的数字频谱.机器视觉机器视觉机器视觉

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