Black plastic identification by hyperspectral imaging in mid-wave infrared

Sensor-based sorters operating in the Near-Infrared (NIR) range are commonly used to sort post-consumer plastics. However, this method fails, when the NIR light is fully absorbed by carbon black pigments, which are present in black plastics. Mid-Wave Infrared (MWIR) is less absorbed by carbon black and therefore provides a promising alternative wavelength range for analyzing black polymers. This study compares MWIR to NIR hyperspectral imaging for classifying black and colored plastic waste. We collected spectral data from five common polymers found in post-consumer packaging, namely High-Density Polyethylene (HDPE), Low-Density Polyethylene (LDPE), Polyethylene Terephthalate (PET), Polypropylene (PP), and Polystyrene (PS). We classified the spectra using several chemometric methods, including a convolutional neural network (CNN). The results quantitatively verify the superior performance of MWIR over NIR for classifying black plastics. MWIR achieved a balanced accuracy of $83.4\%$ compared to $47.5\%$ for NIR, when using a CNN, which outperformed other chemometric methods for all sensors and sample sets. On the other hand, NIR surpasses MWIR by 7 percentage points for colored plastics in balanced accuracy. These findings suggest that MWIR hyperspectral imaging is an effective alternative to NIR hyperspectral imaging for sorting post-consumer packaging waste, especially when the share of black plastics is high.