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Infrared spectroscopy and machine learning for post-consumer plastics recycling
Summary
Scientists combined light-based scanning with artificial intelligence to sort plastic waste more accurately, correctly identifying different plastic types nearly 100% of the time. Better sorting means recycled plastics can be cleaner and more consistent, which matters because low-quality recycling often means plastics get downcycled or end up in landfills and the environment, where they can break down into microplastics. This tool could help the recycling industry produce higher-quality recycled plastic, reducing the amount of new plastic produced and the plastic pollution that eventually finds its way into our water, food, and bodies.
The pollution of plastic materials represents one of the most important environmental challenges, due to the rapid and uncontrolled increase in their production, consumption, and use. Therefore, the recycling and valorization of post-consumer plastics are strategic solutions to mitigate this phenomenon. These processes are subject to rigorous international regulations that require recycled polymers to exhibit physical properties and quality consistent with those of their virgin counterparts. To achieve this goal, a thorough and accurate classification of plastic materials before and after recycling is essential. Infrared (IR) spectroscopy has emerged as a fundamental technique for this purpose, enabling non-destructive identification of polymers through their characteristic vibrational features. However, the high spectral similarity between different plastics often leads to misclassification. To overcome this limitation, in this paper, we integrate IR spectroscopy with advanced Machine Learning (ML) algorithms. We develop and validate a rapid, automated ML-based classifier for the identification of four prevalent plastic types: HDPE-B, HDPE-P, LDPE, and PP. The classification models were trained on a dedicated IR spectral database generated from original experimental measurements. Using the open-source Quasar platform, we performed a comparative analysis between traditional ML algorithms and standard Convolutional Neural Network (CNN) architecture. The study shows that both CNN and classical algorithms, in particular Random Forest (RF), achieve optimal performance in the classification task, with accuracies of 1.000 and 0.998, respectively. Our results demonstrate that the integration of deep learning architectures with IR data significantly enhances the accuracy and reliability of plastic recognition, providing a robust tool to support the industrial transition toward high-quality recycled polymers.