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Deep learning for chemometric analysis of plastic spectral data from infrared and Raman databases
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A novel deep learning architecture called PolymerSpectraDecisionNet was trained to identify common recyclable plastics from infrared and Raman spectral databases. The model outperformed conventional chemometric methods for polymer classification and was designed to handle real-world spectral variability relevant to the plastics recycling industry.
Increasing plastic recycling rates is key to addressing plastic pollution. New technologies such as chemometric analysis of spectral data have shown great promises in improving the plastic sorting efficiency to boost recycling rates. In this work, a novel deep learning architecture, PolymerSpectraDecisionNet (PSDN) was developed, consisting of convolutional neural networks, residual networks and inception networks in a decision tree structure. To better represent the conditions in the plastic recycling industry, the models were built to identify the most widely recycled polymers – polyethylene, polypropylene and polyethylene terephthalate from open-sourced infrared and Raman spectral dataset containing over 20 different polymers. PSDN performed better than end-to-end neural networks, obtaining an accuracy of 0.949 and 0.967 with the Raman and infrared datasets respectively. The use of deep learning can also distinguish between weathered and unaged polymer samples, with accuracies of 0.954 for high density polyethylene and 0.906 for polyethylene terephthalate.
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