[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123469-en":3,"doc-seo-123469-105":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},123469,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Using hyperspectral imaging and machine learning to identify food-contaminated compostable and recyclable plastics","With legislation reducing plastic pollution, compostable plastics are positioned as an alternative for some food packaging and food-service items. Their benefit depends on preventing environmental release and instead processing them with food waste through industrial composting. The key challenge is distinguishing compostable plastics from other plastics in contaminated waste streams, where near-infrared methods struggle. This study applies hyperspectral imaging combined with machine learning to detect and classify food-waste-contaminated compostable plastics, measuring how darkness, size, and contamination level affect performance.","RESEARCH ARTICLE  \nUsing hyperspectral imaging and machine learning to identify food-contaminated compostable and recyclable plastics  \nNutcha Taneepanichskul1 , Helen C. Hailes2 and Mark Miodownik1*  \nHow to cite  \nTaneepanichskul N, Hailes HC, Miodownik M. Using hyperspectral imaging and machine learning to identify food-contaminated compostable and recyclable plastics. UCL Open: Environment. 2025;(7):04 . Available from:  \n[https://doi.org/10.14324/111.444/ucloe.3237](https://doi.org/10.14324/111.444/ucloe.3237)  \nSubmission date: 30 July 2024; Acceptance date: 24 February 2025; Publication date: 02 April 2025  \nPeer review  \nUCL Open: Environment is an open scholarship publication, this article has been peer-reviewed through the journal’s standard open peer-review process. All previous versions of this article and open peer-review reports can be found online in the UCL Open: Environment Preprint server at [https://doi.org/10.14324/1](https://doi.org/10.14324/1)11.444/ucloe.3237  \nCopyright and open access  \n© 2025 The Authors. Creative Commons Attribution Licence (CC BY) 4.0 International licence  \n[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nOpen access  \nThis is an open access article distributed under the terms of the Creative Commons Attribution Licence (CC BY)  \n4.0 [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.  \n*Corresponding author [E-mail: m.miodownik@ucl.ac.uk](E-mail: m.miodownik@ucl.ac.uk)  \n1Mechanical Engineering Department, University College London, London, UK  \n2Chemistry Department, University College London, London, UK  \nAbstract  \nWith the increasing public legislation aimed at reducing plastic pollution, compostable plastics have emerged as an alternative to conventional plastics for some food packaging and food service items. However, the true value of compostable plastics can only be realised if they do not enter the environment as contaminants but instead are processed along with food waste using industrial composting facilities. Distinguishing compostable plastics from other plastics in this waste stream is an outstanding problem. Currently, near-infrared technology is widely used to identify polymers, but it falls short in distinguishing plastics contaminated with food waste. This study investigates the application of hyperspectral imaging to address this challenge, enhancing the detection and sorting of contaminated compostable plastics. By combining hyperspectral imaging with various machine learning algorithms we show it is possible to accurately identify and classify plastic packaging with food waste contamination, achieving up to 99% accuracy. The study also measures the impact of plastic features such as darkness, size and level of contamination on model performance, with darkness having the most significant impact. The developed machine learning model can detect plastic with higher levels of contamination more accurately compared to our previous study. Implementing hyperspectral imaging in waste management systems can significantly increase composting and recycling rates, and improve the quality of recycled products. This advanced approach supports the circular economy by ensuring that both compostable and recyclable plastics are effectively processed and recycled, minimising environmental impact.  \n1 / 21 Using hyperspectral imaging and machine learning to identify food-contaminated compostable and recyclable plastics UCL OPEN ENVIRONMENT  \n[https://doi.org/10.14324/1](https://doi.org/10.14324/1)11.444/ucloe.3237  \nUsing hyperspectral imaging and machine learning to identify food-contaminated compostable and recyclable plastics  \nKeywords: food-contaminated plastics, hyperspectral imaging (HSI), recycling, composting, machine learning, automatic sorting  \nIntroduct","cbCaiugLpTwIRIQd","https://ap.wps.com/l/cbCaiugLpTwIRIQd","pdf",3764970,1,21,"English","en",105,"# Abstract\n## Introduction\n## Near-infrared optical sorting and its limitations\n## Hyperspectral imaging with machine learning","[{\"question\":\"Why is distinguishing compostable plastics in food-contaminated waste streams important?\",\"answer\":\"Compostable plastics provide value only when they are processed with food waste in industrial composting facilities. If they are not correctly separated, they may be sent to landfills, incineration, or unsuitable digestion/recycling routes, lowering composting and recycling rates.\"},{\"question\":\"What problem does near-infrared (NIR) sorting face with food-waste contamination?\",\"answer\":\"Food residues change the reflected NIR spectral signals and introduce additional noise, making it harder for NIR systems to reliably distinguish plastic polymer types under contaminated conditions.\"},{\"question\":\"How does the proposed approach improve identification of contaminated plastics?\",\"answer\":\"The study uses hyperspectral imaging paired with machine learning algorithms to enhance detection and classification of plastic packaging contaminated with food waste, achieving high accuracy and showing that darkness is a major factor affecting model performance.\"}]","Using hyperspectral imaging and machine learning to identify food-contaminated compostable and recyclable plastics | PDF",1785816693,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"using-hyperspectral-imaging-and-machine-learning-to-identify-food-contaminated-compostable-and-recyclable-plastics","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/using-hyperspectral-imaging-and-machine-learning-to-identify-food-contaminated-compostable-and-recyclable-plastics/123469/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is distinguishing compostable plastics in food-contaminated waste streams important?","Question",{"text":75,"@type":76},"Compostable plastics provide value only when they are processed with food waste in industrial composting facilities. If they are not correctly separated, they may be sent to landfills, incineration, or unsuitable digestion/recycling routes, lowering composting and recycling rates.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does near-infrared (NIR) sorting face with food-waste contamination?",{"text":80,"@type":76},"Food residues change the reflected NIR spectral signals and introduce additional noise, making it harder for NIR systems to reliably distinguish plastic polymer types under contaminated conditions.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed approach improve identification of contaminated plastics?",{"text":84,"@type":76},"The study uses hyperspectral imaging paired with machine learning algorithms to enhance detection and classification of plastic packaging contaminated with food waste, achieving high accuracy and showing that darkness is a major factor affecting model performance.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]