Improving the Optical Sorting of Polyester Bioplastics via Reflectance Spectroscopy and Machine Learning Classification Techniques

Several examples of bio-based, compostable polymers have reached commercialization over the past several decades, showing promise for addressing the plastics sustainability crisis. Although their composability minimizes the impact in landfills, employing these materials as single use plastics on a large scale would require exhaustive amounts of resources for crop production to meet current plastic demands. While these thermoplastics are, in principle, mechanically recyclable, the inability to effectively separate them from poly(ethylene terephthalate) impedes practical recycling programs that would enable their reuse. This work explores the potential advancement of optical sorting to enable the classification of polyester bioplastics. Near-infrared (NIR) and mid-infrared (MIR) spectral data were collected for over 500 samples to be used for machine learning classification. Four classification schemes were investigated, including random forest (RF), K nearest neighbors (kNN), and principal component analysis (PCA) coupled with both schemes (PCA-RF and PCA-kNN). Prediction accuracies >92% were demonstrated for both IR regions with the ability to further boost accuracy to >98% by implementing model confidence thresholds. Exploration of sample attributes and their impact on material classification revealed that sample color and opacity have the largest impact on classification in the NIR region, while the MIR region is unimpacted. Additionally, feature importance analysis and feature reduction were carried out, showing that a smaller feature set can be implemented in optical sorters to more efficiently scan samples using only the most informational wavelengths. Finally, synthetic Gaussian noise was introduced into the sample spectra to mimic environmental noise to demonstrate that the classification models have some tolerance to external noise.