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

2021 1 citation ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Shengli Jiang, Zhuo Xu, Medhavi Kamran, Stas Zinchik, Sidike Paheding, Armando G. McDonald, Ezra Bar‐Ziv, Ví­ctor M. Zavala

Summary

Researchers developed a machine-learning framework using convolutional neural networks paired with ATR-FTIR infrared spectroscopy to automatically identify ten different plastic types in mixed plastic waste streams, achieving over 87% accuracy overall. Some plastic types were classified perfectly, offering a promising tool for improving automated sorting in plastic recycling facilities.

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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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