Plastic recycling represents an essential element of strategies aimed at lowering global carbon emissions while supporting a circular plastics economy. However, the effectiveness of current plastic sorting systems remains limited by data scarcity, spectral variability, and the complexity of real world waste streams. This study introduces a CNN-based polymer classification framework that integrates physics-informed spectral simulation, adaptive data augmentation, and Bayesian hyperparameter optimization to enable robust classification under data limited conditions. Our framework combines near-infrared (NIR) spectral data from technical scale measurements with synthetically generated spectra. With only 100 measured spectra per polymer, the proposed framework achieves average balanced accuracies of 0.9739 in multi-target polymer classification tasks. By using technical scale spectral data, this study bridges the gap between laboratory model development and real sorting conditions.
Pletl et al. (Thu,) studied this question.