Hyperspectral imaging, in combination with microscopy, can increase material discrimination compared to standard microscopy. We explored the potential of discriminating pellet microplastic materials using a hyperspectral short-wavelength infrared (SWIR) camera, providing 100 bands in the 1100–1650 nm range, in combination with reflection microscopy. The identification of the most relevant spectral bands helps to increase system cost efficiency. The use of fewer bands reduces memory and processing requirements, and can also steer the development of sustainable, cost-efficient sensors with fewer bands. For this purpose, we present a genetic algorithm to perform band relevance analysis and propose novel algorithm optimizations. The results show that a few spectral bands (between 6 and 9) are sufficient for accurate (>80%) pixel discrimination of all 22 types of microplastic waste, contributing to sustainable development goals (SDGs) such as SDG 6 (‘clean water and sanitation’) or SDG 9 (‘industry, innovation, and infrastructure’). In addition, we study the impact of the classifier method and the width of the spectral response on band selection, neither of which has been addressed in the current state-of-the-art. Finally, we propose a method to steer band selection towards a more balanced distribution of classification accuracy, increasing its applicability in multiclass applications.
