Regression-based Age Prediction of Plastic Waste using Hyperspectral Imaging

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.