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Using ATR-FTIR Spectra and Convolutional Neural Networks for Characterizing Mixed Plastic Waste

2021
Shengli Jiang, Zhuo Xu, Medhavi Kamran, Stas Zinchik, Sidike Paheding, Armando G. McDonald, Ezra Bar‐Ziv, Ví­ctor M. Zavala

Summary

Researchers demonstrated a convolutional neural network framework using ATR-FTIR spectra to classify ten types of plastic commonly found in mixed waste streams, achieving over 87% accuracy. This is a dataset record accompanying the main research article, providing the underlying spectral data used in the study.

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Nervous

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.

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