A Plastic Classification Model Based on Simulated Data

Plastic recycling holds significant potential to reduce global carbon emissions. Despite advances in recycling technologies, challenges such as limited data availability, contamination in sorted materials, and the complexity of real-world material flows continue to hinder progress. This study addresses these issues by introducing a novel approach to plastic classification, leveraging simulated spectral data to reduce reliance on large datasets and improve classification accuracy. Using near-infrared spectroscopy and deep learning models, the framework integrates data augmentation techniques and spectral simulation to augment datasets with synthetic spectra based on a data sample of 25 plastic granules. The proposed classification framework achieves excellent recall and robust balanced accuracy for both binary and multi-target polymer classification with minimal data input (only 50 spectra per class). Thus, the measurement effort is drastically reduced while maintaining an equally high model accuracy. The model significantly outperforms conventional unsupervised approaches. By overcoming the limitations of supervised learning models, the proposed framework provides a scalable and efficient solution for plastics recycling.