Feng Zikang, Zheng Lina, Liu Jia
School of Safety Engineering, China University of Mining and Technology, Xuzhou, People's Republic of China.
Jiangsu Engineering Research Center for Dust Control and Occupational Protection, China University of Mining and Technology, Xuzhou, People's Republic of China; School of Safety Engineering, China University of Mining and Technology, Xuzhou, People's Republic of China; Institute of Occupational Health, China University of Mining and Technology, Xuzhou, People's Republic of China.
Chemosphere. 2023 Jun;325:138312. doi: 10.1016/j.chemosphere.2023.138312. Epub 2023 Mar 10.
The extensive use of plastics leads to the release and diffusion of microplastics. Household plastic products occupy a large part and are closely related to daily life. Due to the small size and complex composition of microplastics, it is challenging to identify and quantify microplastics. Therefore,a multi-model machine learning approach was developed for classification of household microplastics based on Raman spectroscopy. In this study, Raman spectroscopy and machine learning algorithm are combined to realize the accurate identification of seven standard microplastic samples, real microplastics samples and real microplastic samples post-exposure to environmental stresses. Four single-model machine learning methods were used in this study, including Support vector machine (SVM), K-nearest neighbor (KNN), Linear discriminant analysis (LDA), and Multi-layer perceptron (MLP) model. The principal components analysis (PCA) was utilized before SVM, KNN and LDA. The classification effect of four models on standard plastic samples is over 88%, and reliefF algorithm was used to distinguish HDPE and LDPE samples. A multi-model is proposed based on four single models including PCA-LDA, PCA-KNN and MLP. The recognition accuracy of multi-model for standard microplastic samples, real microplastic samples and microplastic samples post-exposure to environmental stresses is over 98%. Our study demonstrates that the multi-model coupled with Raman spectroscopy is a valuable tool for microplastic classification.
塑料的广泛使用导致微塑料的释放和扩散。家用塑料制品占了很大一部分，并且与日常生活密切相关。由于微塑料尺寸小且成分复杂，识别和量化微塑料具有挑战性。因此，开发了一种基于拉曼光谱的多模型机器学习方法用于家用微塑料的分类。在本研究中，将拉曼光谱与机器学习算法相结合，以实现对七种标准微塑料样品、实际微塑料样品以及经受环境应力后的实际微塑料样品的准确识别。本研究使用了四种单模型机器学习方法，包括支持向量机（SVM）、K近邻（KNN）、线性判别分析（LDA）和多层感知器（MLP）模型。在SVM、KNN和LDA之前使用了主成分分析（PCA）。四种模型对标准塑料样品的分类效果超过88%，并使用reliefF算法区分高密度聚乙烯（HDPE）和低密度聚乙烯（LDPE）样品。基于包括PCA-LDA、PCA-KNN和MLP在内的四个单模型提出了一种多模型。该多模型对标准微塑料样品、实际微塑料样品以及经受环境应力后的微塑料样品的识别准确率超过98%。我们的研究表明，与拉曼光谱相结合的多模型是微塑料分类的一种有价值的工具。
