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Study on detection method of microplastics in farmland soil based on hyperspectral imaging technology

Environmental Research 2023 50 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 60 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.

Lijia Xu, Xiaoshi Shi, Jing Feng, Xiaoshi Shi, Lijia Xu, Xiaoshi Shi, Yong He, Lijia Xu, Yanjun Chen, Yanjun Chen, Lijia Xu, Yanqi Feng, Yanqi Feng, Yuchao Wang, Yuchao Wang, Ao Feng, Ao Feng, Xiaoshi Shi, Xiaoshi Shi, Yang Yuping, Yang Yuping, Yanqi Feng, Yanjun Chen, Zhijun Wu, Yong He, Yanqi Feng, Zhijun Wu, Yong He, Yuchao Wang, Yuchao Wang, Yang Yuping, Yuchao Wang, Yuchao Wang, Yuchao Wang, Xiaoshi Shi, Yang Yuping, Yuchao Wang, Yongpeng Zhao Yongpeng Zhao Ning Yang, Yuchao Wang, Yuchao Wang, Yong He, Zhijun Wu, Yong He, Ma Wei, Zhiyong Zou, Yong He, Ning Yang, Ma Wei, Zhijun Wu, Zhiyong Zou, Yuchao Wang, Yong He, Yuchao Wang, Ning Yang, Yongpeng Zhao Yong He, Jing Feng, Ning Yang, Jing Feng, Yongpeng Zhao Yongpeng Zhao

Summary

Researchers developed a method using hyperspectral imaging and machine learning to rapidly detect and classify different types of microplastics in farmland soil. The technology achieved high accuracy in identifying common plastic types like polyethylene and polypropylene in soil samples. Better detection tools like this are essential for monitoring microplastic contamination in agricultural land and understanding its potential impact on food safety.

Polymers

PE PP PVC

Body Systems

Nervous

Microplastics (MPs) in farming soils can have a substantial impact on soil ecology and agricultural productivity, as well as affecting human health and the food chain cycle. As a result, it is vital to study MPs detection technologies that are rapid, efficient, and accurate in agriculture soils. This study investigated the classification and detection of MPs using hyperspectral imaging (HSI) technology and a machine learning methodology. To begin, the hyperspectral data was preprocessed using SG convolution smoothing and Z-score normalization. Second, the feature variables were extracted from the preprocessed spectral data using bootstrapping soft shrinkage, model adaptive space shrinkage, principal component analysis, isometric mapping (Isomap), genetic algorithm, successive projections algorithm (SPA), and uninformative variable elimination. Finally, three support vector machine (SVM), back propagation neural network (BPNN), and one-dimensional convolutional neural network (1D-CNN) models were developed to classify and detect three microplastic polymers: polyethylene, polypropylene, and polyvinyl chloride, as well as their combinations. According to the experimental results, the best approaches based on three models were Isomap-SVM, Isomap-BPNN, and SPA-1D-CNN. Among them, the accuracy, precision, recall and F1_score of Isomap-SVM were 0.9385, 0.9433, 0.9385 and 0.9388, respectively. The accuracy, precision, recall and F1_score of Isomap-BPNN were 0.9414, 0.9427, 0.9414 and 0.9414, respectively, while the accuracy, precision, recall and F1_score of SPA-1D-CNN were 0.9500, 0.9515, 0.9500 and 0.9500, respectively. When their classification accuracy was compared, SPA-1D-CNN had the best classification performance, with a classification accuracy of 0.9500. The findings of this study shown that the SPA-1D-CNN based on HSI technology can efficiently and accurately identify MPs in farmland soils, providing theoretical backing as well as technical means for real-time detection of MPs in farmland soils.

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