人工智能
预处理器
天然有机质
模式识别(心理学)
拉曼光谱
卷积神经网络
计算机科学
微塑料
深度学习
人工神经网络
鉴定(生物学)
生物系统
光谱特征
数据预处理
有机质
机器学习
化学
遥感
环境化学
物理
地质学
光学
植物
有机化学
生物
作者
Seunghyeon Lee,Hee-Won Jeong,Seok Min Hong,Daeun Yun,Jiye Lee,Eun‐Ju Kim,Kyung Hwa Cho
出处
期刊:Water Research
[Elsevier]
日期:2023-11-01
卷期号:246: 120710-120710
被引量:11
标识
DOI:10.1016/j.watres.2023.120710
摘要
Several preprocessing procedures are required for the classification of microplastics (MPs) in aquatic systems using spectroscopic analysis. Procedures such as oxidation, which are employed to remove natural organic matter (NOM) from MPs, can be time- and cost-intensive. Furthermore, the identification process is prone to errors due to the subjective judgment of the operators. Therefore, in this study, deep learning (DL) was applied to improve the classification accuracies for mixtures of microplastic and natural organic matter (MP-NOM). A convolutional neural network (CNN)-based DL model with a spatial attention mechanism was adopted to classify substances from their Raman spectra. Subsequently, the classification results were compared with those obtained using conventional Raman spectral library software to evaluate the applicability of the model. Additionally, the crucial spectral band for training the DL model was investigated by applying gradient-weighted class activation mapping (Grad-CAM) as a post-processing technique. The model achieved an accuracy of 99.54%, which is much higher than the 31.44% achieved by the Raman spectral library. The Grad-CAM approach confirmed that the DL model can effectively identify MPs based on their visually prominent peaks in the Raman spectra. Furthermore, by tracking distinctive spectra without relying solely on visually prominent peaks, we can accurately classify MPs with less prominent peaks, which are characterized by a high standard deviation of intensity. These findings demonstrate the potential for automated and objective classification of MPs without the need for NOM preprocessing, indicating a promising direction for future research in microplastic classification.
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