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Using ATR-FTIR Spectra and Convolutional Neural Networks for Characterizing Mixed Plastic Waste
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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.
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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Using ATR-FTIR Spectra and Convolutional Neural Networks for Characterizing Mixed Plastic Waste
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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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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.