As the aluminium industry faces increasing pressure to decarbonize, there is a growing demand from end users for higher recycled content in finished metal products. The necessity for clean scrap as input material has become paramount, especially given the challenges posed by the diverse range of aluminium alloys and their bonding properties. This complexity often complicates recycling efforts, leading to substantial losses in material quality. A focal point of this research is the identification and sorting of “twitch,” a major grade of aluminium scrap that requires precise alloy separation to enhance recycling content. The presentation emphasizes sensor-based sorting technologies as critical components in achieving high-quality scrap products suitable for direct recycling. Specifically, we explore three innovative sensor technologies: X-Ray Transmission (XRT), Deep Learning/AI, and Laser Induced Breakdown Spectroscopy (LIBS).