Shape matters for microplastics, but its definition, particularly for hyperspectral imaged microplastics, remains ambiguous and inexplicit, leading to incomparability across data. Hyperspectral imaging is a common approach for quantification, yet no unambiguous microplastic shape classification exists. We conducted an expert-based survey and proposed a set of clear and concise shapes (fiber, rod, ellipse, oval, sphere, quadrilateral, triangle, free-form, and unidentifiable). The categories were validated on images of 11,042 microplastics from four environmental compartments (seven matrices: indoor air; wastewater influent, effluent, and sludge; marine water; stormwater; and stormwater pond sediments), by inviting five experts to score each shape. We found that the proposed shapes were well defined, representative, and distinguishable to the human eye, especially for fiber and sphere. Ellipse, oval, and rod were though less distinguishable but dominated in all water and solid matrices. Indoor air held more unidentifiable, an abstract shape that appeared mostly for particles below 30 μm. This study highlights the need for assessing the recognizability of chosen shape categories prior to reporting data. Shapes with a clear and stringent definition would increase comparability and reproducibility across data and promote harmonization in microplastic research.
Keywords: ground truth; hyperspectral image; manual classification; microplastic; pixelization; shape.