A machine learning based classification models for plastic recycling using different wavelength range spectrums

Currently, the use and production of plastic are on the rising trend, leading to the increase in waste generation and consumption of raw materials, making it one of the most significant issues facing densely populated cities. As a result, plastic waste management is becoming one of society's primary concerns, as it has direct effects on the environment and sustainability of urban areas. However, the identification and classification of the different types of plastic remains a challenge, as current techniques still face limitations. Unfortunately, identification technologies cannot classify many types of plastic, and often a particular technology is used to classify certain types of plastic. In this study, we propose a classification methodology that analyses the use of different machine learning algorithms based on the infrared spectrum of polymers. This proposed classification methodology will be able to identify spectrums obtained from different wavelength ranges, including near-infrared spectroscopy (NIR) and mid-infrared spectroscopy (MWIR). Moreover, this study proposes a solution to the classification of black plastic, as well as common recycled plastics such as Polypropylene (PP), Polystyrene (PS), Polyethylene (PE), Polyvinyl chloride (PVC), and Polyethylene terephthalate (PET). Experiments were conducted using eleven machine learning classification algorithms, and a comparative analysis of their performance was presented. The results indicate that five out of eleven classifiers achieved over 95% on the four metrics analyzed (accuracy, precision, recall and f1-score), with the Multi-layer Perceptron (MLP) Classifier achieving 99.72% accuracy, 99.35% precision, 99.82% recall, and 99.58% f1-score.