环境微塑料分类的混合深度学习框架:集成基于CNN的光谱特征提取和变压器模型
Fang Li1, Jiayu Tian1, Xuetao Guo2
1Institute of Quality Standard and Testing Technology, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100095, China; Beijing Municipal Key Laboratory of Agriculture Environment Monitoring, Beijing, 100097, China.
Environmental pollution (Barking, Essex : 1987)
|August 15, 2025
概括
结合CNN和变压器的新混合深度学习模型使用FTIR光谱准确分类环境微塑料 (MP). 这种先进的方法可以提高各种环境样本的识别准确性和稳定性.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 数据科学数据科学数据科学
背景情况:
- 从FTIR光谱进行精确的微塑料 (MP) 分类受到现有模型的光谱变异性和局限性的阻碍.
- 现有的方法很难有效地捕捉本地和全球的光谱特征.
研究的目的:
- 开发一种强大而准确的方法来使用里埃变换红外光谱 (FTIR) 分类环境微塑料 (MP).
- 在MP识别中应对光谱变异性和环境气候变化的挑战.
主要方法:
- 开发了一个混合深度学习框架,集成卷积神经网络 (CNN) 和变压器模型.
- 该CNN组件提取本地光谱特征,而变压器模块捕获远程依赖.
- 构建了来自各种环境矩阵的17种聚合物类型的多样化的光谱数据集.
主要成果:
- 拟议的CNN-Transformer模型在验证集上实现了95.77%的分类准确性.
- 该模型在MP分类中表现优于传统的机器学习方法.
- 通过50个独立试验证实了强度,显示出稳定和可重复的性能.
结论:
- 混合CNN-变压器架构为在各种环境环境中快速识别MP提供了可靠和可扩展的解决方案.
- 这种方法增强了特征提取,提高了分类的稳定性,并确保了通用性.
- 这些发现对污染监测和对微塑料污染的监管评估有影响.
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