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相关概念视频

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

955
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
955

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FRDA:使用可解释AI进行基于FTIR的微塑料分类的指纹区域数据增强.

Xinyu Yan1, Zhi Cao2, Alan Murphy2

  • 1Software Research Institute, Technological University of the Shannon: Midlands, Ireland; Luoyang Institute of Science and Technology, China.

The Science of the total environment
|July 6, 2023
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概括
此摘要是机器生成的。

使用机器学习的海洋微塑料识别通过新的数据增强方法得到了改进. 该技术通过专注于关键光谱区域来解决不平衡的数据集,提高水污染监测的识别准确性.

关键词:
数据增强数据增强数据预处理数据的预处理.深度学习是一种深度学习.美国的FTIR是FTIR.机器学习 机器学习微塑料的识别 微塑料的识别

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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:

  • 海洋微塑料 (MP) 污染对水生生态系统和人类健康构成重大风险.
  • 机器学习 (ML) 模型,特别是那些使用减弱总反射里埃变换红外光谱 (ATR-FTIR) 的机器学习模型,对于MP识别至关重要.
  • 不平衡和不足的样本数据,特别是对于共聚物和混合物,对于训练准确的ML模型来说是一个重大挑战.

研究的目的:

  • 为了应对MP识别中不平衡数据集的挑战.
  • 通过有效的数据增强来提高MP识别ML模型的性能.
  • 通过可解释的人工智能 (XAI) 和高斯混合模型 (GMM) 来识别MP识别的有影响力的光谱区域.

主要方法:

  • 利用可解释的人工智能 (XAI) 和高斯混合模型 (GMM) 来分析ATR-FTIR光谱并确定MP识别的关键光谱区域.
  • 开发了一种基于指纹区域的新型数据增强 (FRDA) 方法,以基于确定的光谱区域生成合成FTIR数据.
  • 补充了现有的MP数据集,使用FRDA方法新生成的数据.

主要成果:

  • 该FRDA方法有效地产生了新的FTIR数据,增加了不平衡的MP数据集.
  • 使用XAI和GMM进行的分析显示,特定的光谱区域对于区分MP类型至关重要.
  • 评估表明,FRDA在改善ML模型性能方面显著优于现有的光谱数据增强技术.

结论:

  • 拟议的FRDA方法是增加MP数据集的有效方法,特别是在解决样本不平衡和复杂性方面.
  • 识别和利用指纹光谱区域可以提高基于ML的MP识别的准确性.
  • 这项工作为改善海洋微塑料污染的分析和监测提供了宝贵的工具.