文献类型：期刊文献

英文题名：Accurate characterization of mix plastic waste using ATR-FTIR spectroscopy and machine learning methods

作者：Zhou, Ziang[1];Shao, Hengxuan[1];Liu, Baining[1,2];Xie, Yufeng[1];Wang, Wanqing[1,2]

第一作者：周哲

通讯作者：Wang, WQ[1];Wang, WQ[2]

机构：[1]Beijing Union Univ, Biochem Engn Coll, Beijing, Peoples R China;[2]Beijing Key Lab Biomass Waste Resource Utilizat, Beijing, Peoples R China

第一机构：北京联合大学生物化学工程学院

通讯机构：[1]corresponding author), Beijing Union Univ, Biochem Engn Coll, Beijing, Peoples R China;[2]corresponding author), Beijing Key Lab Biomass Waste Resource Utilizat, Beijing, Peoples R China.|[1141726]北京联合大学生物化学工程学院;[11417]北京联合大学;

年份：2026

卷号：21

期号：2

外文期刊名：PLOS ONE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001691382700042)】；

基金：This work was supported by the National Key Research and Development Program of China under Grant Number No. 2022YFC3902401.

语种：英文

摘要：The global proliferation of plastic waste presents significant environmental challenges, with effective sorting of complex waste streams being a critical bottleneck for recycling. Conventional sorting methods struggle with dark-colored plastics, a major limitation for near-infrared (NIR) systems, and require costly pre-cleaning of contaminated items. This study develops a robust methodology using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with optimized machine learning to overcome these key limitations. Two models were established. Model 1 focused on the high-accuracy identification of 10 common plastic types, demonstrating 97.1% accuracy on an independent test set that included challenging dark and black samples. Model 2 addresses the pivotal challenge of identifying oil-contaminated plastics without any physical pre-cleaning. It innovatively employs Independent Component Analysis (ICA) for spectral unmixing, successfully separating the plastic's signal from the oil contaminant's. The extracted plastic spectra were then processed through an optimized workflow, achieving a remarkable accuracy of 92.5%. These results demonstrate that ATR-FTIR, empowered by advanced chemometric strategies like ICA and optimized machine learning, provides a powerful, non-destructive solution for sorting diverse and complex plastic waste. This work pioneers a viable pathway for the direct, algorithm-driven characterization of contaminated plastics, offering a promising approach to enhance the automation and efficiency of plastic recycling systems.