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 |
DOI | 10.1016/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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