Classification of black plastic types by hyperspectral imaging based on long-wave infrared emission spectroscopy

Abstract

Identification of black plastics poses a significant challenge in recycling due to the absorptive nature of carbon black additives. This work introduces a method where hyperspectral imaging in the long-wave infrared regime is used to distinguish between twelve samples of commercially available black plastics encompassing nine distinct polymer types. The spectral scanner comprises a scanning Fabry-Pérot interferometer and a thermal camera based on an uncooled microbolometer detector sensitive to wavelengths from 8 μm to 15 μm. A principal component model is combined with k-nearest neighbors to differentiate between plastic samples in hyperspectral images. The model successfully classifies five (PET, POM, PMMA, PA6, and PA66) out of nine black polymers, and the overall accuracy of the model is A=73.1 %.

Keywords

• Hyperspectral imaging
• Fabry-pérot interferometry
• Thermography
• Classification of black plastics
• Principal component analysis