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The use of recycled waste is increasing nowadays, and it is a major concern to use recycled post-consumer waste in food packaging. The use of recycled waste will reduce waste and will contribute to sustainability and a circular economy. In this study, a comparison has been made between recycled and virgin Polypropylene (PP) materials that have been used and going to be used in food packaging respectively. Herein, virgin and recycled PP samples were analyzed, and hundreds of Volatile Organic Compounds (VOCs), odorous, and semi-VOCs have been observed with the help of Gas chromatography-mass spectrometry (GC-MS). These samples were analyzed two times to get high efficiency. To classify the VOCs and odorous compounds within the Virgin Polyethylene (Vpet) and Recycled Polyethylene (Rpet) classes, four machine learning algorithms were applied: Random Forest (RF), XGBOOST, Support Vector Machine (SVM), and Gradient Boosted Decision Tree (GBDT). Among these algorithms, Random Forest achieved the highest accuracy. Additionally, the Mean Decrease Impurity method was utilized to determine the feature importance in the classification process.

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Polyethylene Classification into Virgin and Recycled: A Machine Learning Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Pragti Saini,
  • Shubham Joshi,
  • Sampat Singh Bhati,
  • Millie Pant

摘要

The use of recycled waste is increasing nowadays, and it is a major concern to use recycled post-consumer waste in food packaging. The use of recycled waste will reduce waste and will contribute to sustainability and a circular economy. In this study, a comparison has been made between recycled and virgin Polypropylene (PP) materials that have been used and going to be used in food packaging respectively. Herein, virgin and recycled PP samples were analyzed, and hundreds of Volatile Organic Compounds (VOCs), odorous, and semi-VOCs have been observed with the help of Gas chromatography-mass spectrometry (GC-MS). These samples were analyzed two times to get high efficiency. To classify the VOCs and odorous compounds within the Virgin Polyethylene (Vpet) and Recycled Polyethylene (Rpet) classes, four machine learning algorithms were applied: Random Forest (RF), XGBOOST, Support Vector Machine (SVM), and Gradient Boosted Decision Tree (GBDT). Among these algorithms, Random Forest achieved the highest accuracy. Additionally, the Mean Decrease Impurity method was utilized to determine the feature importance in the classification process.