Using ATR-FTIR spectra and convolutional neural networks for characterizing mixed plastic waste

We present a convolutional neural network (CNN) framework for classifying different types of plastic ma-terials that are commonly found in mixed plastic waste (MPW) streams. The CNN framework uses exper-imental ATR-FTIR (attenuated total reflection-Fourier transform infrared spectroscopy) spectra to classify ten different plastic types. An important aspect of this type of spectral data is that it can be collected in real-time; as such, this approach provides an avenue for enabling the high-throughput characterization of MPW. The proposed CNN architecture (which we call PlasticNet) uses a Gramian angular representation of the spectra. We show that this 2-dimensional (2D) matrix representation highlights correlations between different frequencies (wavenumber) and leads to significant improvements in classification accuracy, com-pared to the direct use of spectra (a 1D vector representation). We also demonstrate that PlasticNet can reach an overall classification accuracy of over 87% and can classify certain plastics with 100% accuracy. Our framework also uses saliency maps to analyze spectral features that are most informative. (c) 2021 Elsevier Ltd. All rights reserved.