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Dual Imaging Approach Using Laser-Induced Fluorescence and Hyperspectral Reflectance for Automated Sorting of White Polyamide in Mixed Waste
Summary
This research examined corporate sustainability reporting on plastic commitments, analyzing whether stated plastic reduction pledges from major consumer goods companies are matched by measurable progress in waste reduction. The study finds significant gaps between corporate plastic commitments and verifiable outcomes, highlighting greenwashing risks in voluntary corporate plastic governance.
In automated recycling systems, accurately sorting mixed-material waste remains a significant challenge, particularly when materials such as wood, metals, and polymers exhibit similar visual appearances. White polyamide polymer is widely used in industrial components due to its durability and chemical resistance, yet its recovery from waste streams is often hindered by optical similarities to light-colored wood and oxidized or coated metals. This study presents a dual imaging approach that combines laser-induced fluorescence (LIF) using ultraviolet excitation with hyperspectral imaging (HSI) of diffuse reflectance under broadband illumination (400-1000 nm). The fluorescence experiments revealed that the white polyamide polymer exhibited a strong significant wavelength at approximately 740 nm, distinctly separating it from wood and metal, alongside a less prominent secondary response at 443 nm. However, in the visible range from about 480 to 630 nm, the fluorescence responses of wood and polymer substantially overlapped, underscoring the importance of targeting the near-infrared (NIR) emission for effective polymer discrimination. Complementary diffuse reflectance HSI data analyzed through histogram techniques identified optimal wavelengths near 480 nm and 840 nm that enhanced contrast between the polymer, wood, and metal, guiding the design of simplified multispectral systems. This dual-modality imaging strategy integrates molecular fluorescence sensitivity with detailed reflectance profiling to achieve improved material discrimination, paving the way for practical, automated sorting solutions. The insights gained also support the future development of conventional aerial camera systems equipped with optimized filters and illumination sources to monitor and manage polymer waste accumulation on larger environmental scales.