Detection of microplastics in sea salt using hyperspectral imaging and machine learning methods: Pollution control in the Mediterranean sea as a case study Medina García, Miriam Martínez Domingo, Miguel Ángel Valero Benito, Eva María Cuadros Rodríguez, Luis Jiménez Carvelo, Ana María Project (PCM-00042) is funded by the «Next Generation EU» Recovery Fund through the 'Recovery, Transformation and Resilience Plan' and co-financed by the Consejería de Universidad, Investigación e Innovación de la Administración de la Junta de Andalucía (Regional Ministry of University, Research and Innovation of Andalusia). Grant (RYC2021-031993-I) funded by MICIU/AEI/501100011033 and “European Union NextGeneration EU/PRTR”. Universidad de Granada / CBUA. Microplastics represent 80% of the marine waste, becoming one of the main problems worldwide today, one of the reasons they have been categorised as the 10th greatest threat in the World Economic Forum’s Global Risks Report 2024. To address this issue, many recognised organisations have developed action plans for monitorization, mitigation and prevention of microplastic contamination. This includes the development of analytical methods for the detection, characterisation and quantification of these contaminants. In this regard, this work presents a novel approach for the direct detection and analytical evaluation of microplastics in sea salt sampled from solar sea saltworks. These factories act as a natural ’pre-concentrator’ of solid pollutants, and sea salt is thus a good indicator of their presence in the marine environment. The developed methodology is based on the application of hyperspectral imaging a non-destructive/non-invasive analytical technique, in combination with machine learning methods, to detect five of the most common microplastics (PE, PET, PS, PP, PVC) in natural sea salt samples collected directly from a solar saltworks located on the Mediterranean coast of southern Spain. For this purpose, some key features were assessed to develop the methodology, including sample bank generation, particle size determination, imaging conditions, and others. Finally, once the HSI analyses were performed directly on the solid salt samples, partial least square-discriminant analysis was applied to develop a classification model capable of identifying salt-containing pixels and thus detecting µP pollution. 2025-06-06T07:52:51Z 2025-06-06T07:52:51Z 2025-06-03 journal article M. Medina–García et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy Volume 343 (2025) 126528. https://doi.org/10.1016/j.saa.2025.126528 https://hdl.handle.net/10481/104507 10.1016/j.saa.2025.126528 eng info:eu-repo/grantAgreement/EC/NextGenerationEU/PCM-00042 http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier