首页|Rapid detection of colored and colorless macro-and micro-plastics in complex environment via near-infrared spectroscopy and machine learning

Rapid detection of colored and colorless macro-and micro-plastics in complex environment via near-infrared spectroscopy and machine learning

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Rapid detection of colored and colorless macro-and micro-plastics in complex environment via near-infrared spectroscopy and machine learning
To better understand the migration behavior of plastic fragments in the environment,de-velopment of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary.However,most of the studies had focused only on col-ored plastic fragments,ignoring colorless plastic fragments and the effects of different en-vironmental media(backgrounds),thus underestimating their abundance.To address this issue,the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analy-sis(PLS-DA),extreme gradient boost,support vector machine and random forest classifier.The effects of polymer color,type,thickness,and background on the plastic fragments clas-sification were evaluated.PLS-DA presented the best and most stable outcome,with higher robustness and lower misclassification rate.All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm.A two-stage modeling method,which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background,was proposed.The method presented an accuracy higher than 99％in different backgrounds.In summary,this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.

Colorless microplasticsNear-infrared hyperspectral imagingPlastic identificationPartial least squares discriminantanalysisMachine learning

Hui-Huang Zou、Pin-Jing He、Wei Peng、Dong-Ying Lan、Hao-Yang Xian、Fan Lü、Hua Zhang

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Institute of Waste Treatment & Reclamation,College of Environmental Science and Engineering,Tongji University,Shanghai 200092,China

Shanghai Institute of Pollution Control and Ecological Security,Shanghai 200092,China

Colorless microplastics Near-infrared hyperspectral imaging Plastic identification Partial least squares discriminant analysis Machine learning

2025

10.1016/j.jes.2023.12.004

环境科学学报(英文版)
中科院生态环境研究中心

环境科学学报(英文版)

影响因子：0.862
ISSN：1001-0742
年,卷(期)：2025.147(1)