Regression-based Age Prediction of Plastic Waste using Hyperspectral Imaging
Felix Kronenwett,
Pia Klingenberg,
Georg Maier,
Thomas Längle,
Elke Metzsch-Zilligen,
Jürgen Beyerer
Kapitel/Beitrag aus dem Buch: Beyerer, J et al. 2023. OCM 2023 - 6th International Conference on Optical Characterization of Materials, March 22nd – 23rd, 2023, Karlsruhe, Germany : Conference Proceedings.
In order to enable high quality recycling of polypropylene
(PP) plastic, additional classification and separation into the
degree of degradation is necessary. In this study, different PP
plastic samples were produced and degraded by multiple extrusion
and thermal treatment. Using near infrared spectroscopy,
the samples were examined and regression models were trained
to predict the degree of aging. The models of the multiple extruded
samples showed high accuracy, despite only minor spectral
changes. The accuracy of the models of the thermally aged
samples varied with the design of the training set due to the
non-linear aging process, but showed sufficient accuracy in prediction.