Hyperspectral imaging-based classification of post-consumer thermoplastics for plastics recycling using artificial neural network

The increasing global output of plastic waste has created a critical need for cost-effective and highly accurate methods of plastic recycling. One of the major challenges in recycling plastics is maintaining the purity of plastic streams, which is essential for secondary plastics to compete with virgin plastics in the market. In this context, this paper presents a novel approach for classifying different polymers in a complex plastic waste stream using a hyperspectral imaging system in the near-infrared (NIR) range (900-1700 nm). The study focuses on the applications of entropy and contrast stretching for image segmentation to identify distinct objects in a given hyperspectral image. The classification process is carried out using a two-layer feedforward network with sigmoid hidden neurons and SoftMax output neurons. The performance of the proposed method was evaluated using a dataset comprising hyperspectral images of unknown plastic waste stream samples, and it was able to achieve an accuracy of about 89.5%. The results of this study demonstrate the potential of using hyperspectral imaging in the NIR range as a cost-effective and highly accurate method for classifying different polymers in plastic waste streams. This approach can be further developed for use in real-world recycling facilities, contributing to the advancement of the plastic waste management field.