https://doi.org/10.1140/epjs/s11734-026-02398-5
Regular Article
Microplastic detection and characterization in different matrixes by computed tomography
1
Nuclear Engineering Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
2
Federal Institute of Paraná-Campus Pinhais, Paraná, Brazil
a
This email address is being protected from spambots. You need JavaScript enabled to view it.
Received:
29
August
2025
Accepted:
18
May
2026
Published online:
30
May
2026
Abstract
Microplastic (MP) pollution represents a growing environmental threat, particularly in terrestrial and coastal ecosystems. Characterizing MPs is challenging due to their heterogeneous shapes, sizes, and chemical compositions, as well as their interactions with complex environmental matrices. This study evaluates X-ray microtomography (microCT) combined with linear regression models (LRM), a machine learning approach within artificial intelligence (AI), for high-resolution detection and morphometric characterization of MPs in soils. Controlled experiments used sandy, humus-rich, and mangrove soils spiked with polyethylene terephthalate (PET) fragments (~ 1 mm). MicroCT provided non-destructive three-dimensional imaging, from which volume, surface area, elongation, flatness, anisotropy, and sphericity were extracted. Image processing, including filtering and morphological corrections, enhanced particle delineation and data accuracy. LRMs predicted interdependent morphometric parameters, with performance assessed using mean squared error (MSE), mean absolute error (MAE), and the coefficient of determination (R2). Results showed high predictive accuracy for sandy and humus-rich soils (average R2 ≈ 0.93–0.94), whereas mangrove soils exhibited lower and less stable performance (average R2 ≈ 0.69), reflecting matrix heterogeneity. These findings indicate that soil properties strongly influence microCT-based MP characterization and that linear regression may be unreliable in complex matrices; however, combining high-resolution imaging with predictive modeling provides a more robust approach for accurately assessing MPs across diverse environments, supporting improved environmental monitoring and informing methodological strategies tailored to specific substrates.
© The Author(s) 2026
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
