A study published in *Interactions* presents a plastic classification system combining a Near-Infrared (NIR) sensor with machine learning for automated identification. Spectral reflectance data were collected from polypropylene (PP), polyethylene terephthalate (PET), low-density polyethylene (LDPE), and baseline samples. Multiple machine learning models were evaluated, and the best-performing methods were combined to improve classification performance. The system was implemented using an Arduino-based sensing setup and a Python-based machine learning pipeline. Experimental results demonstrated an accuracy of over 98% during real-time testing, indicating the potential of the proposed low-cost system for automated plastic sorting and waste management applications.