Convolutional neural network with near-infrared spectroscopy for plastic discrimination

Plastic pollution is a global issue of increasing health concern, thus requiring innovative waste management. In particular, there is a need for advanced methods to identify and classify the different types of plastics. Near-infrared spectroscopy is currently operational in some waste-sorting facilities, yet remains challenging to discriminate different black plastics because black targets have low reflectance in some spectral regions. Here we used partial least squares discrimination analysis, soft independent modeling of class analogy, linear discriminant analysis and convolutional neural network to classify the plastics. We analyzed 159 plastic samples, including 84 black plastics, made of high impact polystyrene, acrylonitrile butadiene styrene, high-density polyethylene, polyethylene terephthalate, polyamide 66, polycarbonate and polypropylene. Results show that the convolutional neural network model yielded an accuracy up to 98%, whereas other models displayed accuracy of 57-70%. Overall, convolutional neural network analysis of infrared plastic data is promising to solve the bottleneck problem of black plastic discrimination.

Automated summary

The study utilized various methods to classify plastics, with the convolutional neural network model showing the highest accuracy, offering hope for solving the bottleneck of black plastic discrimination.