Yan-Ling Xie , Qi-Zhi Su , Qin-Bao Lin , Huai-Ning Zhong , Dan Li , Ben Dong
{"title":"Integration of UV-vis spectroscopy and machine learning for identification of recycled polyethylene terephthalate","authors":"Yan-Ling Xie , Qi-Zhi Su , Qin-Bao Lin , Huai-Ning Zhong , Dan Li , Ben Dong","doi":"10.1016/j.fpsl.2025.101463","DOIUrl":null,"url":null,"abstract":"<div><div>The circular economy has driven renewed interest in polyethylene terephthalate (PET) recycling, including recycled PET for food packaging, with a focus on reducing production costs and meeting social development goals. This study presents a novel approach to classifying virgin and recycled PET using an economically viable ultraviolet-visible spectroscopy combined with machine learning algorithms. The results show that Baseline Removal (RMBL) is the optimal preprocessing method for binary classification, and Principal Component Analysis (PCA) combined with Random Forest (RF) is the most effective binary classification model to distinguishes between virgin and 100 % recycled PET. To further improve the capability to detect samples of low recycled content, a multi-classification model was then developed. This approach enables the detection of recycled PET content as low as 10 %, providing a quantitative possibility for the classification. This study demonstrates the effectiveness of this approach and has significant implications for sustainable recycling practices.</div></div>","PeriodicalId":12377,"journal":{"name":"Food Packaging and Shelf Life","volume":"48 ","pages":"Article 101463"},"PeriodicalIF":8.5000,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food Packaging and Shelf Life","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S221428942500033X","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The circular economy has driven renewed interest in polyethylene terephthalate (PET) recycling, including recycled PET for food packaging, with a focus on reducing production costs and meeting social development goals. This study presents a novel approach to classifying virgin and recycled PET using an economically viable ultraviolet-visible spectroscopy combined with machine learning algorithms. The results show that Baseline Removal (RMBL) is the optimal preprocessing method for binary classification, and Principal Component Analysis (PCA) combined with Random Forest (RF) is the most effective binary classification model to distinguishes between virgin and 100 % recycled PET. To further improve the capability to detect samples of low recycled content, a multi-classification model was then developed. This approach enables the detection of recycled PET content as low as 10 %, providing a quantitative possibility for the classification. This study demonstrates the effectiveness of this approach and has significant implications for sustainable recycling practices.
期刊介绍:
Food packaging is crucial for preserving food integrity throughout the distribution chain. It safeguards against contamination by physical, chemical, and biological agents, ensuring the safety and quality of processed foods. The evolution of novel food packaging, including modified atmosphere and active packaging, has extended shelf life, enhancing convenience for consumers. Shelf life, the duration a perishable item remains suitable for sale, use, or consumption, is intricately linked with food packaging, emphasizing its role in maintaining product quality and safety.