LED vs. halogen illumination for hyperspectral imaging in the NIR range of space-derived waste materials

Circular economic models play an important role in the context of waste management, which is essential for reducing environmental impact and preserving resources. For recycling procedures to be optimized, materials must be accurately identified and classified. In space, waste accumulation poses risks to missions and increases debris in orbit. Therefore, effective waste management systems are essential for mitigating hazards and reducing the environmental footprint of space activities. Hyperspectral Imaging (HSI) in the Near Infrared (NIR) range offers a non-invasive solution for real-time material analysis in space, where traditional sample preparation is challenging. This study examines the use of two distinct illumination sources, LED and Halogen light, for hyperspectral data collection to classify space-derived waste materials, including foams, technical textiles, and plastics, while also comparing their performance. Machine learning algorithms were employed to develop a classification model capable of automatically discriminating among the investigated material categories based on their spectral signatures. Furthermore, a calibration transfer strategy was explored to enable the transferability of models developed under one illumination condition to datasets acquired with a different light source, improving the robustness and flexibility of the proposed approach. LED lighting is particularly beneficial in scenarios demanding energy efficiency and stability. At the same time, halogen illumination is more suitable in contexts where a broader spectral range is crucial for accurate material differentiation. The results demonstrate that the integration of NIR hyperspectral imaging with machine learning and calibration transfer techniques enables reliable material identification under varying illumination conditions. Overall, the findings highlight the potential of this approach to support automated classification systems and advance space waste recycling strategies.