Microplastic pollution presents major environmental and health challenges, requiring accurate identification and quantification to assess its distribution and impact. Conventional methods such as chromatography and spectrometry provide precise results but are destructive, time-consuming, and resource intensive. Hyperspectral Imaging in the Near-Infrared range (HSI-NIR) offers a non-destructive alternative by capturing both spectral and spatial information, though analysis of its large, noisy datasets remains difficult. This study introduces an analytical pipeline combining HSI-NIR with optimized preprocessing and a machine-learning-based Multi-Layer Perceptron (MLP) model for pixel-wise classification of microplastic particles. The shallow MLP architecture effectively handles high-dimensional data using predefined spectral features. The approach was applied to samples from Lanzarote Island, the Wadden Sea, and the Waal and Rhine rivers, accurately identifying polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), and polystyrene (PS). The MLP model outperformed Support Vector Machines, Random Forests, and Partial Least Squares Discriminant Analysis in polymer identification. PE and PP were dominant across all sites, with PET and PS in lower proportions. River samples showed higher microplastic concentrations in the Rhine than in the Waal, with polymer composition stable across depths. Code available at: https://github.com/petroshatt/hyperplastics. • A non-destructive workflow identifies microplastics using NIR hyperspectral imaging • The method enables semi-quantitative analysis of polymer distribution in water samples • A deep learning MLP model improves pixel-wise polymer classification accuracy • The pipeline is validated on samples from marine, riverine, and coastal environments • Polyethylene and polypropylene are dominant in all tested aquatic systems
Chatzitoulousis et al. (Sun,) studied this question.