Cost-effective approaches for microplastic pellets characterization using a machine learning tool
Microplastics, including pellets, are a persistent pollutant on beaches that pose relevant ecological and environmental challenges. Their widespread presence in marine and coastal environments endangers ecosystems, threatens marine life, and risks entering the food chain. Effective microplastic mana...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125002390 |
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| author | V.M. Scarrica P. Cocozza G. Anfuso A. Staiano G. Bonifazi A. Rizzo S. Serranti |
| author_facet | V.M. Scarrica P. Cocozza G. Anfuso A. Staiano G. Bonifazi A. Rizzo S. Serranti |
| author_sort | V.M. Scarrica |
| collection | DOAJ |
| description | Microplastics, including pellets, are a persistent pollutant on beaches that pose relevant ecological and environmental challenges. Their widespread presence in marine and coastal environments endangers ecosystems, threatens marine life, and risks entering the food chain. Effective microplastic management requires reliable methods for their identification and classification, yet the high cost of required equipment hinders large-scale implementation. Artificial intelligence offers a promising solution for polymer analysis. While machine learning techniques have demonstrated potential in automating microplastic classification, existing approaches often rely on complex models requiring numerous input variables, limiting their practical application. This paper introduces a simplified methodology for pellet polymer classification using a Random Forest model requiring a limited set of variables for training. The approach reduces model complexity while maintaining high classification performance, emphasizing simplicity, speed and efficiency. The method was tested on different pellet samples collected from the coasts of Spain, Portugal and Vulcano Island (Italy). The results highlight the robustness of the proposed model and its suitability to be applied in diverse environmental contexts. By balancing accuracy with computational efficiency, the proposed approach represents a practical tool for pellet classification. This streamlined methodology can offer a significant step forward in microplastic management and pollution mitigation, contributing to the development of cost-effective, scalable solutions for addressing the environmental impacts of microplastics. |
| format | Article |
| id | doaj-art-05d1355fea0e457da7d342252ab412fc |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-05d1355fea0e457da7d342252ab412fc2025-08-20T05:05:13ZengElsevierEcological Informatics1574-95412025-12-019010323010.1016/j.ecoinf.2025.103230Cost-effective approaches for microplastic pellets characterization using a machine learning toolV.M. Scarrica0P. Cocozza1G. Anfuso2A. Staiano3G. Bonifazi4A. Rizzo5S. Serranti6Department of Sciences and Technology, University of Naples Parthenope Naples, ItalyDepartment of Chemical Engineering, Materials & Environment, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, ItalyDepartment of Earth Sciences, Faculty of Marine and Environmental Sciences, University of Cádiz, 11510 Puerto Real, SpainDepartment of Sciences and Technology, University of Naples Parthenope Naples, ItalyDepartment of Chemical Engineering, Materials & Environment, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, ItalyDepartment of Earth and Geoenvironmental Sciences, University of Bari Aldo Moro, Bari, Italy; Interdepartmental Research Center for Coastal Dynamics, University of Bari Aldo Moro, Bari, ItalyDepartment of Chemical Engineering, Materials & Environment, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy; Corresponding author.Microplastics, including pellets, are a persistent pollutant on beaches that pose relevant ecological and environmental challenges. Their widespread presence in marine and coastal environments endangers ecosystems, threatens marine life, and risks entering the food chain. Effective microplastic management requires reliable methods for their identification and classification, yet the high cost of required equipment hinders large-scale implementation. Artificial intelligence offers a promising solution for polymer analysis. While machine learning techniques have demonstrated potential in automating microplastic classification, existing approaches often rely on complex models requiring numerous input variables, limiting their practical application. This paper introduces a simplified methodology for pellet polymer classification using a Random Forest model requiring a limited set of variables for training. The approach reduces model complexity while maintaining high classification performance, emphasizing simplicity, speed and efficiency. The method was tested on different pellet samples collected from the coasts of Spain, Portugal and Vulcano Island (Italy). The results highlight the robustness of the proposed model and its suitability to be applied in diverse environmental contexts. By balancing accuracy with computational efficiency, the proposed approach represents a practical tool for pellet classification. This streamlined methodology can offer a significant step forward in microplastic management and pollution mitigation, contributing to the development of cost-effective, scalable solutions for addressing the environmental impacts of microplastics.http://www.sciencedirect.com/science/article/pii/S1574954125002390MicroplasticPelletPolymer classificationMachine learningRandom Forest |
| spellingShingle | V.M. Scarrica P. Cocozza G. Anfuso A. Staiano G. Bonifazi A. Rizzo S. Serranti Cost-effective approaches for microplastic pellets characterization using a machine learning tool Ecological Informatics Microplastic Pellet Polymer classification Machine learning Random Forest |
| title | Cost-effective approaches for microplastic pellets characterization using a machine learning tool |
| title_full | Cost-effective approaches for microplastic pellets characterization using a machine learning tool |
| title_fullStr | Cost-effective approaches for microplastic pellets characterization using a machine learning tool |
| title_full_unstemmed | Cost-effective approaches for microplastic pellets characterization using a machine learning tool |
| title_short | Cost-effective approaches for microplastic pellets characterization using a machine learning tool |
| title_sort | cost effective approaches for microplastic pellets characterization using a machine learning tool |
| topic | Microplastic Pellet Polymer classification Machine learning Random Forest |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125002390 |
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