在基于拉曼光谱和卷积神经网络的淡水沉积物中检测微塑料
Shusheng Liu1,2, Yinlong Luo3, Qihang Wan4
1College of Mechanics and Engineering Science, Hohai University, Nanjing 210098, China.
The journal of physical chemistry. B
|November 13, 2025
概括
一种新方法使用拉曼光谱和卷积神经网络 (CNN) 准确检测淡水沉积物中的微塑料 (MP). 这种技术达到94.27%的准确性,优于其他模型,有助于控制污染.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 材料科学 材料科学 材料科学
背景情况:
- 微塑料 (MP) 污染在水生环境中越来越令人担忧.
- 微塑料积聚在沉积物中,其复杂的组成阻碍了分析.
- 有效的监测方法对于PM污染控制至关重要.
研究的目的:
- 开发一种新的检测方法,用于淡水沉积物中的微塑料.
- 使用拉曼光谱与卷积神经网络 (CNN) 结合,用于准确的MP识别.
- 评估丹湖沉积物中的微塑料的来源.
主要方法:
- 密度分离和真空辅助过被用于从沉积物样本中分离MP.
- 通过使用MP和沉积物混合物的拉曼光谱来训练和测试CNN模型.
- 将CNN模型的性能与支持矢量机 (SVM) 和随机森林 (RF) 模型进行了比较.
主要成果:
- 开发的基于CNN的方法在淡水沉积物中的MPs中实现了94.27%的高识别精度.
- 与SVM和RF模型相比,CNN模型表现出卓越的性能.
- 对丹湖沉积物的应用表明,当地人为活动是MPs的主要来源.
结论:
- 拉曼光谱与CNN相结合,提供了一种准确而有效的方法,用于检测复杂的淡水沉积物矩阵中的微塑料.
- 这些发现凸显了先进分析技术在环境监测中的有效性.
- 了解MP源对于制定有针对性的减缓策略来减少水污染至关重要.
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