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基于机器学习的 (微) 塑料光谱重建和分类的工作流.

Yanlong Liu1, Ziwei Zhao1, Chunyang Hu1

  • 1Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China.

Chemosphere
|November 29, 2024
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概括
此摘要是机器生成的。

本研究介绍了一种机器学习工作流程,用于识别微塑料 (MP). 它使用自动编码器和V形卷积神经网络增强了光谱数据,提高了环境分析的识别精度.

关键词:
自动编码器 自动编码器卷积神经网络是一种卷积神经网络.标识 识别 识别 识别微塑料是一种微塑料.重建重建的重建工作

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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:

  • 精确识别微塑料 (MP) 对于环境监测至关重要.
  • 现有的MP的光谱分析方法经常受到光谱干扰的阻碍,影响识别的准确性.
  • 人工智能 (AI) 的进步为开发自动化和更精确的MP识别技术提供了潜力.

研究的目的:

  • 为微塑料的光谱重建和识别开发一个完全基于机器学习的工作流.
  • 通过使用先进的重建模型来提高MPs光谱的质量.
  • 通过复杂的分类算法来提高MP识别的准确性.

主要方法:

  • 开发了两个光谱重建模型:自编码器 (AE) 和V型卷积神经网络 (VCNN).
  • 实施了四种分类模型:决策树,随机森林,线性支向量机 (LSVM) 和1D卷积神经网络.
  • 对萨维茨基-戈莱算法进行重建模型的评估,并对原始和重建数据集的分类性能进行比较.

主要成果:

  • 与AE和萨维茨基-戈莱算法相比,VCNN显示了优异的光谱重建性能 (R2 = 0.965).
  • 线性支向量机 (LSVM) 实现了最高的分类准确性,在VCNN重建的数据上达到98.00%.
  • 综合工作流在真实环境数据集上显示出实际意义,最高1准确率为71.43%,最高3准确率超过90%.

结论:

  • 拟议的机器学习工作流程有效地提高了微塑料的光谱质量,并提高了识别精度.
  • 集成VCNN和LSVM为自动化微塑料分析提供了强大的解决方案.
  • 这种方法具有很大的潜力,可以在环境研究中推进计算机辅助的微塑料识别.