We present a convolutional neural network (CNN) framework for classifying different types of plastic materials that are commonly found in mixed plastic waste (MPW) streams. The CNN framework uses experimental ATR-FTIR (attenuated total reflection-Fourier transform infrared spectroscopy) spectra to classify ten different plastic types. We show that the approach reaches accuracies of over 87% and that some plastic types can be perfectly classified.
Using ATR-FTIR Spectra and Convolutional Neural Networks for Characterizing Mixed Plastic Waste
03 May 2021, Version 2
This content is a preprint and has not undergone peer review at the time of posting.