Support Vector Machine-Based Logics for Exploring Bromine and Antimony Content in ABS Plastic from E-Waste by Using Reflectance Spectroscopy

Brominated Flame Retardants (BFRs), widely used in Electrical and Electronic Equipment (EEE), pose severe health and environmental risks and complicate recycling at the end-of-life stage, calling for innovative, sustainable detection and sorting solutions. In this context, new strategies that are efficient, reliable, sustainable, and cost-effective are required. This study investigates Short-Wave Infrared (SWIR) spectroscopy for detecting brominated plastics and quantifying bromine (Br) and antimony (Sb) content in Cathode-Ray Tube (CRT) e-waste. X-Ray Fluorescence (XRF) provided reference measurements, while Support Vector Machine (SVM) models were trained on reflectance spectra acquired with a portable spectroradiometer. The SVM–Discriminant Analysis models achieved near-perfect classification, with 100% accuracy in distinguishing samples above and below the regulatory thresholds for Br (2000 mg/kg) and Sb (8354 mg/kg). SVM regression yielded excellent quantitative predictions, with R 2 P = 0.996 and RMSEP = 2671 mg/kg for Br, and R 2 P = 0.999 and RMSEP = 1056 mg/kg for Sb. These performances confirm the robustness of SWIR spectroscopy for rapid, non-destructive monitoring of hazardous plastics, even in highly heterogeneous waste streams. The integration of SWIR spectroscopy with machine learning supports selective recycling and safer resource recovery, directly contributing to United Nations Sustainable Development Goals on Decent Work and Economic Growth (SDG 8), Industry, Innovation and Infrastructure (SDG 9), and Responsible Consumption and Production (SDG 12).