Raman-validated image learning enables microplastic prescreening and electronic-waste candidate prioritization

IF 11 1区工程技术 Q1 ENGINEERING, CHEMICAL

npj Clean Water Pub Date : 2026-07-31 DOI:10.1038/s41545-026-00614-2

Huase Ou, Yang Hou, Ming Wen, Yirong Wen, Yuanying Lu, Zecheng Liao

{"title":"Raman-validated image learning enables microplastic prescreening and electronic-waste candidate prioritization","authors":"Huase Ou, Yang Hou, Ming Wen, Yirong Wen, Yuanying Lu, Zecheng Liao","doi":"10.1038/s41545-026-00614-2","DOIUrl":null,"url":null,"abstract":"Microplastic (MP) identification in wastewater treatment remains constrained by the need to verify visually selected particles individually by micro-Raman spectroscopy. Here, we developed a Raman-validated microscopic image-learning workflow for a full-scale wastewater treatment plant receiving domestic sewage and electronic waste (e-waste) dismantling wastewater. The dataset contained 1554 images from 1136 particles, including 654 Raman-confirmed MPs and 482 non-MPs. For high-recall MP/non-MP prescreening, EfficientNet-B0 with 384 × 384-pixel inputs and particle-max aggregation retained 94.6 ± 3.6% of true MPs, reduced 31.9 ± 8.3% of non-MPs, and excluded 16.4 ± 4.9% of candidate particles before Raman confirmation. Within Raman-confirmed MPs, texture-focused ResNet18 prioritized e-waste-associated candidates defined by operational source-proxy labels based on polymer identity, morphology, and sample context. The top 20% particle-level candidates contained 73.3 ± 12.4% e-waste-associated candidates, with an enrichment factor of 1.322 ± 0.225. This conservative workflow does not replace Raman spectroscopy, but supports workload reduction and source-proxy candidate prioritization in complex wastewater matrices.","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"41 1","pages":""},"PeriodicalIF":11.0000,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Clean Water","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1038/s41545-026-00614-2","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}

引用次数: 0

Abstract

Microplastic (MP) identification in wastewater treatment remains constrained by the need to verify visually selected particles individually by micro-Raman spectroscopy. Here, we developed a Raman-validated microscopic image-learning workflow for a full-scale wastewater treatment plant receiving domestic sewage and electronic waste (e-waste) dismantling wastewater. The dataset contained 1554 images from 1136 particles, including 654 Raman-confirmed MPs and 482 non-MPs. For high-recall MP/non-MP prescreening, EfficientNet-B0 with 384 × 384-pixel inputs and particle-max aggregation retained 94.6 ± 3.6% of true MPs, reduced 31.9 ± 8.3% of non-MPs, and excluded 16.4 ± 4.9% of candidate particles before Raman confirmation. Within Raman-confirmed MPs, texture-focused ResNet18 prioritized e-waste-associated candidates defined by operational source-proxy labels based on polymer identity, morphology, and sample context. The top 20% particle-level candidates contained 73.3 ± 12.4% e-waste-associated candidates, with an enrichment factor of 1.322 ± 0.225. This conservative workflow does not replace Raman spectroscopy, but supports workload reduction and source-proxy candidate prioritization in complex wastewater matrices.

查看原文本刊更多论文

拉曼验证的图像学习可以实现微塑料预筛选和电子废物候选优先级

废水处理中的微塑料（MP）鉴定仍然受到需要通过微拉曼光谱单独验证视觉选择颗粒的限制。在这里，我们开发了一个拉曼验证的微观图像学习工作流程，用于接收生活污水和电子废物（电子废物）拆除废水的全规模污水处理厂。该数据集包含来自1136个粒子的1554张图像，其中包括654张拉曼确认的MPs和482张非MPs。对于高召回率的MP/非MP预筛选，使用384 × 384像素输入和粒子max聚集的EfficientNet-B0保留了94.6±3.6%的真实MP，减少了31.9±8.3%的非MP，并在拉曼确认前排除了16.4±4.9%的候选颗粒。在拉曼确认的MPs中，以纹理为重点的ResNet18优先考虑与电子废物相关的候选材料，这些候选材料由基于聚合物特性、形态和样品环境的操作源代理标签定义。前20%的颗粒级候选物含有73.3±12.4%的电子垃圾相关候选物，富集因子为1.322±0.225。这种保守的工作流程不能取代拉曼光谱，但可以在复杂的废水矩阵中减少工作量和源代理候选优先级。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

npj Clean Water Environmental Science-Water Science and Technology

CiteScore

15.30

自引率

2.60%

发文量

审稿时长

5 weeks

期刊介绍： npj Clean Water publishes high-quality papers that report cutting-edge science, technology, applications, policies, and societal issues contributing to a more sustainable supply of clean water. The journal's publications may also support and accelerate the achievement of Sustainable Development Goal 6, which focuses on clean water and sanitation.