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Recent advances in the application of machine learning methods to improve identification of the microplastics in environment

文献类型：期刊论文

作者Lin, Jia-yu1,2; Liu, Hong-tao2; Zhang, Jun3
刊名CHEMOSPHERE
出版日期2022-11-01
卷号307页码:7
ISSN号0045-6535
关键词Machine learning Microplastics Identification Quantification Support vector machine Artificial neural network
DOI10.1016/j.chemosphere.2022.136092
通讯作者Liu, Hong-tao(liuht@igsnrr.ac.cn) ; Zhang, Jun(zjun@glut.edu.cn)
英文摘要Environmental pollution by microplastics (MPs) is a significant and complex global issue. Existing MPs identi-fication 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.
WOS关键词PLASTIC DEBRIS ; CLASSIFICATION ; RECOGNITION ; WATER ; FISH
资助项目National Natural Science Foundation of China[51978642] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA28130300]
WOS研究方向Environmental Sciences & Ecology
语种英语
出版者PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号WOS:000860693800001
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/185143]  
专题中国科学院地理科学与资源研究所
通讯作者Liu, Hong-tao; Zhang, Jun
作者单位1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
3.Guilin Univ Technol, Guangxi Key Lab Environm Pollut Control Theory & T, Guilin 541004, Peoples R China
推荐引用方式
GB/T 7714		 Lin, Jia-yu,Liu, Hong-tao,Zhang, Jun. Recent advances in the application of machine learning methods to improve identification of the microplastics in environment[J]. CHEMOSPHERE,2022,307:7.
APA Lin, Jia-yu,Liu, Hong-tao,&Zhang, Jun.(2022).Recent advances in the application of machine learning methods to improve identification of the microplastics in environment.CHEMOSPHERE,307,7.
MLA Lin, Jia-yu,et al."Recent advances in the application of machine learning methods to improve identification of the microplastics in environment".CHEMOSPHERE 307(2022):7.

入库方式： OAI收割

来源：地理科学与资源研究所

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