微塑料
鉴定(生物学)
人工智能
支持向量机
计算机科学
机器学习
生化工程
工程类
化学
环境化学
生物
植物
作者
Jia-yu Lin,Hongtao Liu,Jun Zhang
出处
期刊:Chemosphere
[Elsevier]
日期:2022-11-01
卷期号:307: 136092-136092
被引量:32
标识
DOI:10.1016/j.chemosphere.2022.136092
摘要
Environmental pollution by microplastics (MPs) is a significant and complex global issue. Existing MPs identification methods have demonstrated significant limitations such as low resolution, long imaging time, and limited particle size analysis. New and improved methods for MPs identification are topical research areas, with machine learning (ML) algorithms proven highly useful in recent years. Critical literature reviews on the latest developments in this area are limited. This study closes this gap and summarizes the progress made over the last 10 years in using ML algorithms for identifying and quantifying MPs. We identified diverse combinations of ML methods and fundamental techniques for MPs identification, such as Fourier-transform infrared spectroscopy, Raman spectroscopy, and near-infrared spectroscopy. The most widely used ML model is the support vector machine, which effectively improves the conventional analysis method for spectral quality defects and improves detection accuracy. Artificial neural network models exhibit improved recognition effects, with shorter detection times and better MPs recognition efficiency. Our review demonstrates the potential of ML in improving the identification and quantification of MPs.
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