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Short-wave infrared hyperspectral imaging of microplastics: Effects of chemical and physical processes on spectral signatures and detection capabilities
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Researchers evaluated short-wave infrared hyperspectral imaging for rapid microplastic detection and polymer identification, testing the effects of various physical and chemical weathering agents on spectral signatures and finding the technique effective for identifying multiple polymer types in complex samples.
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, HNO 3 , SDS, NaOH, H 2 O 2 , 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 H 2 O 2 ), 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. • MP detection and identification were studied with hyperspectral imaging. • Seven polymers in two size ranges were exposed to physical and chemical agents. • Spectral changes varied by polymer type, with PP and PE showing minimal effect • Higher spectral distortions in smaller size ranges highlight their role in training • PerClass Mira's visualization feature enhanced MP detection and identification
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