Microplastics in the rough: using data augmentation to identify plastics contaminated by water and plant matter†

Joseph C. Shirley, Kobiny Antony Rex, Hassan Iqbal, Christian G. Claudel and Carlos R. Baiz
{"title":"Microplastics in the rough: using data augmentation to identify plastics contaminated by water and plant matter†","authors":"Joseph C. Shirley, Kobiny Antony Rex, Hassan Iqbal, Christian G. Claudel and Carlos R. Baiz","doi":"10.1039/D4SU00612G","DOIUrl":null,"url":null,"abstract":"<p >Microplastics are present in nearly all environments. The detection of microplastics in the field is an important step toward understanding and regulating the proliferation of plastic waste, particularly in natural environments. Real-time surveys require robust instruments, rapid acquisition, and minimal processing. Near infrared (NIR) spectroscopy is an ideal technique to detect polymer composition regardless of spectral interference by water and/or organic matter. Here we report a fiber-based NIR instrument designed for simple and efficient spectral acquisition of consumer plastic particles across a range of sizes. Data augmentation with measured interferent spectra has been used to generate machine-learning based classification models that can identify polymer compositions in plastic particles that are wet and/or mixed in with organic plant material. These models achieve 98.5% accuracy on synthetic data and 86.4% accuracy when transferred to spectra of plastic particles of nine common polymers with particle sizes as small as 500 μm. Our model paves the way for the development of equipment to perform real-time surveys of microplastic compositions in the field.</p>","PeriodicalId":74745,"journal":{"name":"RSC sustainability","volume":" 4","pages":" 1886-1899"},"PeriodicalIF":0.0000,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/su/d4su00612g?page=search","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"RSC sustainability","FirstCategoryId":"1085","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2025/su/d4su00612g","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Microplastics are present in nearly all environments. The detection of microplastics in the field is an important step toward understanding and regulating the proliferation of plastic waste, particularly in natural environments. Real-time surveys require robust instruments, rapid acquisition, and minimal processing. Near infrared (NIR) spectroscopy is an ideal technique to detect polymer composition regardless of spectral interference by water and/or organic matter. Here we report a fiber-based NIR instrument designed for simple and efficient spectral acquisition of consumer plastic particles across a range of sizes. Data augmentation with measured interferent spectra has been used to generate machine-learning based classification models that can identify polymer compositions in plastic particles that are wet and/or mixed in with organic plant material. These models achieve 98.5% accuracy on synthetic data and 86.4% accuracy when transferred to spectra of plastic particles of nine common polymers with particle sizes as small as 500 μm. Our model paves the way for the development of equipment to perform real-time surveys of microplastic compositions in the field.

Abstract Image

粗糙中的微塑料:使用数据增强来识别被水和植物污染的塑料†
微塑料几乎存在于所有环境中。在该领域检测微塑料是了解和调节塑料废物扩散的重要一步,特别是在自然环境中。实时调查需要强大的仪器、快速采集和最少的处理。近红外(NIR)光谱是检测聚合物成分的理想技术,不受水和/或有机物质的光谱干扰。在这里,我们报告了一种基于纤维的近红外仪器,该仪器设计用于简单有效地获取各种尺寸的消费塑料颗粒的光谱。使用测量的干涉光谱进行数据增强已被用于生成基于机器学习的分类模型,该模型可以识别潮湿和/或与有机植物材料混合的塑料颗粒中的聚合物成分。这些模型在合成数据上的精度达到98.5%,在9种常见聚合物的塑料颗粒光谱上的精度达到86.4%,颗粒尺寸小至500 μm。我们的模型为在现场进行微塑料成分实时调查的设备的开发铺平了道路。
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
CiteScore
0.60
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信