Study on Rapid Recognition of Microplastics Based on Infrared Spectroscopy

doi:10.3964/j.issn.1000-0593(2022)11-3501-06

	Study on Rapid Recognition of Microplastics Based on Infrared Spectroscopy
	Wu Xue 1,2; Feng Wei-wei2,3,4 ; Cai Zong-qi 2,3; Wang Qing2,3
发表期刊	SPECTROSCOPY AND SPECTRAL ANALYSIS
ISSN	1000-0593
	2022-11-01
卷号	42 期号:11 页码:3501-3506
关键词	Microplastics Near infrared spectrum XGBoost SVM
DOI	10.3964/j.issn.1000-0593(2022)11-3501-06
通讯作者	Feng Wei-wei(wwfeng@yic.ac.cn)
英文摘要	The combination of spectroscopic technology and machine learning algorithm for rapid identification of microplastics provides great technical support for microplastics' field detection, a new field that has attracted great attention. Nirs detection technology has the characteristics of fast detection speediness, highly sensitization, damage less, and can be directly detected without sample pretreatment, widely used in chemical analysis quality detection and other fields. This paper compares support vector machine (SVM) and Extreme Gradient Boosting (XGBoost) , two machine learning classification algorithms based on the infrared spectrum, to build a classification model for high-speed and effective recognition of microplastics. Acrylonitrile butadiene styrene(ABS) Polyacrylonitrile (PAN), Polycarbonate (PC), Polyethylene glycol terephthalate(PET), Polymethyl methacrylate (PMMA), Polypropylene (PP), Polystyrene (PS), Polyvinyl chloride (PVC), Thermoplastic polyurethane (TPU) Ethylene-vinyl acetate copolymer (EVA) Polybutylene terephthalate (PBT) Polycaprolactone (PCL) Polyethersulfone (PES) Polylactic acid (PLA) Polyoxymethylene (POM) Polyphenylene Oxide (PPO) Polyphenylene sulfide (PPS), Poly tetra fluoroethylene (PTFE), polyvinyl alcohol (PVA) Styrenic Block Copolymers (SBS) 20 standard samples of microplastics were collected by using A miniature near-infrared spectrum. In order to prevent overfitting, 1 260 microplastic samples were collected, each sample containing 512 data points. The XGBoost algorithm was used to rank the importance of the logarithmic data points, and a total of 65 data points which greatly influenced the recognition accuracy were extracted. SVM algorithm and XGBoost algorithm are respectively used to establish a microplastic fast recognition model for 65 data points extracted after dimensionality reduction, and GridSearchCV is used to select the hyperparameters that have a great influence on XGBoost algorithm to determine n_estimators, learning_rate, The optimal hyperparameters for mM_child_weigh, max_depth, and gamma are 700, 0. 07, 1,1, 0. 0, respectively. In order to improve the model's stability, recognition rate and generalization ability, a 10-fold cross-validation and confusion matrix were used to evaluate the model. The results show that the recognition accuracy of the XGBoost model is 97% , while that of the SVM model is 95%. The accuracy of the XGBoost model is better than the SVM model. In conclusion, the overall performance of the XGBoost model was better than that of the SVM model, which provides technical support for rapid identification of actual microplastics.
收录类别	SCI
语种	英语
研究领域[WOS]	Spectroscopy
WOS记录号	WOS:000891902300027
引用统计
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/32420
专题	中国科学院海岸带环境过程与生态修复重点实验室海岸带生物学与生物资源利用重点实验室
通讯作者	Feng Wei-wei
作者单位	1.Harbin Inst Technol, Weihai 264209, Weihai, Peoples R China 2.Chinese Acad Sci, Yantai Inst Coastal Zone, Key Lab Coastal Environm Proc & Ecol Restorat, Yantai 264003, Peoples R China 3.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Wu Xue,Feng Wei-wei,Cai Zong-qi,et al. Study on Rapid Recognition of Microplastics Based on Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2022,42(11):3501-3506.
APA	Wu Xue,Feng Wei-wei,Cai Zong-qi,&Wang Qing.(2022).Study on Rapid Recognition of Microplastics Based on Infrared Spectroscopy.SPECTROSCOPY AND SPECTRAL ANALYSIS,42(11),3501-3506.
MLA	Wu Xue,et al."Study on Rapid Recognition of Microplastics Based on Infrared Spectroscopy".SPECTROSCOPY AND SPECTRAL ANALYSIS 42.11(2022):3501-3506.