[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122910-en":3,"doc-seo-122910-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122910,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning","The increasing prevalence of nanoplastics in the environment highlights the urgent need for reliable detection and monitoring. Identification remains difficult because nanoplastics are extremely small and exhibit complex composition, while many existing techniques are optimized for microplastics. This study integrates highly reflective substrates with machine learning to identify nanoplastics from Raman spectroscopy data. A random-forest model trained on processed peak extraction and retention features achieves an average accuracy of 98.8%. Validation using tap-spiked water samples reaches over 97% identification accuracy and demonstrates applicability to real environmental matrices such as rainwater.","Citation for published version:  \nXie, L, Luo, S, Liu, Y, Ruan, X, Gong, K, Ge, Q, Li, K, Valev, VK, Liu, G & Zhang, L 2023, 'Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning', Environmental Science and Technology, vol. 57, no. 46, pp. 18203-18214. [https://doi.org/10.1021/acs.est.3c03210](https://doi.org/10.1021/acs.est.3c03210)  \nDOI:  \n10.1021/acs.est.3c03210  \nPublication date:  \n2023  \nDocument Version  \nPeer reviewed version  \nLink to publication  \nThis document is the Accepted Manuscript version of a Published Work that appeared in final form in Environmental Science and Technology, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see [https://pubs.acs.org/doi/10.1021/acs.est.3c03210](https://pubs.acs.org/doi/10.1021/acs.est.3c03210)  \nUniversity of Bath  \nAlternative formats  \nIf you require this document in an alternative format, please contact: [openaccess@bath.ac.uk](openaccess@bath.ac.uk)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 05. Jul. 2024  \n1 Automatic Identification of Individual Nanoplastics by  \n2 Raman spectroscopy based on Machine Learning  \n3 Lifang Xie1,2,3, Siheng Luo4,5, Yangyang Liu1,2,3, Xuejun Ruan1,2,3, Kedong Gong1,2,3, Kejian Li1,2,3, Qiuyue Ge1,2,3, 4 Ventsislav Kolev Valev6, Guokun Liu4,5, Liwu Zhang1,2,3 *  \n5 1Department of Environmental Science & Engineering, Fudan University, Shanghai, 200433, Peoples’ Republic of  \n6 China.  \n7 2Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, 8 Peoples’ Republic of China.  \n9 3Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, Peoples' Republic of China.  \n10 4State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, Xiamen  \n11 University, Xiamen 361005, and P. R. China.  \n12 5Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine  \n13 Environmental Chemistry & Toxicology, Xiamen University, Xiamen 361102, China.  \n14 6Centre for Photonics and Photonic Materials and Centre for Nanoscience and Nanotechnology, Department of  \n15 Physics, University of Bath, Claverton Down, Bath BA2 7AY, U.K.  \n16  \n17 ABSTRACT  \n18 The increasing prevalence of nanoplastics in the environment underscores the need for effective  \n19 detection and monitoring techniques. Current methods mainly focus on microplastics, while  \n20 accurate identification of nanoplastics is challenging due to their small size and complex  \n21 composition. In this work, we combined highly reflective substrates and machine learning to  \n22 accurately identify nanoplastics using Raman spectroscopy. Our approach established Raman  \n23 spectroscopy datasets of nanoplastics, incorporated peak extraction and retention data processing, 24 and constructed a random forest model that achieved an average accuracy of 98.8% in identifying  \n25 nanoplastics. We validated our method with tap spiked water samples, achieving over 97%  \n26 identification accuracy, and demonstrated the applicability of our algorithm to real-world  \n27 environmental samples through experiments on rainwater, detecting nanoscale Polystyrene (PS) and  \n28 Polyvinyl chloride (PVC) . Despite the challenges of processing low-quality nanoplastic Raman 29 spectra and complex environmental samples, our study demonstrated the potential of using random 30 forests to identify and dis","cbCaicSUoLMyu7bj","https://ap.wps.com/l/cbCaicSUoLMyu7bj","pdf",2609603,1,29,"English","en",105,"# 1. Introduction","[{\"question\":\"Why is nanoplastic identification more challenging than microplastic identification?\",\"answer\":\"Nanoplastics are much smaller and have complex compositions, making accurate identification difficult with methods that are often designed for microplastics.\"},{\"question\":\"What method does the study propose for identifying individual nanoplastics?\",\"answer\":\"The approach combines Raman spectroscopy with machine learning, using processed Raman peak/retention information and a random-forest model for classification.\"},{\"question\":\"How was the proposed method validated and what performance was achieved?\",\"answer\":\"The method was validated using tap-spiked water samples, achieving over 97% identification accuracy, and it was also tested on real environmental samples such as rainwater to detect nanoscale polystyrene and polyvinyl chloride.\"}]","Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning 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is nanoplastic identification more challenging than microplastic identification?","Question",{"text":75,"@type":76},"Nanoplastics are much smaller and have complex compositions, making accurate identification difficult with methods that are often designed for microplastics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What method does the study propose for identifying individual nanoplastics?",{"text":80,"@type":76},"The approach combines Raman spectroscopy with machine learning, using processed Raman peak/retention information and a random-forest model for classification.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the proposed method validated and what performance was achieved?",{"text":84,"@type":76},"The method was validated using tap-spiked water samples, achieving over 97% identification accuracy, and it was also tested on real environmental samples such as rainwater to detect nanoscale polystyrene and polyvinyl 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