Comparative Analysis of Hyperspectral Classification Strategies for Robotic Sorting of Non-Contaminated Hospital Plastic Waste: A System Design and Methodology Framework

Non-contaminated plastic waste from healthcare facilities is largely non-infectious and rich in recoverable polymers, yet it is routinely incinerated for lack of an automated sorting process suited to the clinical setting. This paper presents a sensor fusion system design and a comparative hyperspectral classification methodology for that stream. Near-infrared hyperspectral imaging, an RGB line-scan camera, and an inductive metal sensor are integrated on a collaborative robot platform, combining polymer-type identification, visual object detection, and metal discrimination in a late-fusion pipeline. Two hyperspectral classification strategies are specified and compared across labeling cost, robustness, interpretability, and stage of system maturity: reference-spectra distance matching, which needs no labeled training data, and supervised machine learning, which gains robustness as the labeled dataset grows. The two are ordered as successive development phases, the first bootstrapping the second. A throughput and spectral analysis establishes the sensor specification required for production line speeds, which the pipeline architecture accommodates without change to the classification methodology. The regulatory scope of the recoverable fraction under German waste legislation is treated as an explicit design input, determining the material classes targeted and the open-set rejection behavior required of the classifiers. Open design parameters and a validation pathway are identified.