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ML_IR_v9.pdf (3.29 MB)

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

preprint
revised on 30.04.2021, 13:09 and posted on 03.05.2021, 06:59 by Shengli Jiang, Zhuo Xu, Medhavi Kamran, Stas Zinchik, Sidike Paheding, Armando McDonald, Ezra Bar-Ziv, Victor Zavala

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.

History

Email Address of Submitting Author

sjiang87@wisc.edu

Institution

University of Wisconsin-Madison

Country

United States

ORCID For Submitting Author

0000-0002-5421-5803

Declaration of Conflict of Interest

No conflict of interest

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