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 because no automated sorting process suited to the clinical setting has been established. This paper presents the design of a sensor fusion system and a hyperspectral classification methodology for this stream; experimental results are not yet available and will be reported in a follow-up study. Near-infrared hyperspectral imaging, an RGB line-scan camera, and an inductive metal sensor are integrated on a collaborative robot platform and combined in a late-fusion pipeline for polymer identification, object detection, and metal discrimination. Two hyperspectral classification strategies are specified: reference-spectrum distance matching, which requires no labeled training data, and supervised machine learning, which depends on a labeled dataset. A conceptual comparison based on their methodological properties and on published evidence leads to their use as successive development phases, with the first strategy helping to build the training set for the second. An analytical assessment based on sensor datasheet values indicates the sensor specification required for production line speeds, which the pipeline accommodates without changes to the classification methodology. The regulatory scope of the recoverable fraction under German waste legislation is treated as a design input that determines the target material classes and the open-set rejection behavior required of the classifiers. Open design parameters and a staged validation pathway are identified.