Short-wave infrared hyperspectral imaging of microplastics: Effects of chemical and physical processes on spectral signatures and detection capabilities

This study investigated the performance of hyperspectral imaging (HSI) in the short-wave infrared (SWIR) band (900–1620 nm) coupled with perClass Mira software for rapid detection and polymer identification of microplastics (MPs). Various physical and chemical agents were applied to simulate MPs in environmental samples and test the limits and capabilities of SWIR-HSI under challenging conditions. Spectral signatures of seven different MP polymers (polypropylene, polystyrene, high-density polyethylene, low-density polyethylene, acrylonitrile butadiene styrene, thermoplastic elastomer, and thermoplastic polyurethane) were analyzed before and after treatment with physical and chemical agents (KOH, HNO₃, SDS, NaOH, H₂O₂, Fenton's reagent, coating with oil and water, and heating to 100°C). Six different MP colours and two different size ranges (1–5 mm, 300–500 µm) were also included in the testing. Results showed that the degree of spectral alteration depended on size and polymer type. Appearance of a distinct peak near 1400 nm was observed on exposure to chemical agents (NaOH, Fenton's reagent, and H₂O₂), likely attributed to the hydroxyl group. Polypropylene and polyethylene exhibited fewer spectral alterations compared to polystyrene and thermoplastics. Additionally, MPs in the smaller size ranges were more susceptible to chemical and physical agents, necessitating the inclusion of these variations in the training set. The visualization feature in perClass Mira shows immense promise in enhancing the classification algorithm, achieving an overall accuracy of 94.4 % in post-treatment samples. This study aims to establish a groundwork for rapid and efficient MP classification in environmental samples using SWIR-HSI technology.