This work presents a sustainable analytical workflow for the detection and typification of microplastics (MPs) in environmental matrices using Near Infrared Hyperspectral Imaging (NIR-HSI) combined with chemometrics. The proposed methodology enables rapid, non-destructive, and solvent-free analysis, aligning with green analytical principles. A hierarchical classification strategy based on Partial Least Squares Discriminant Analysis (PLS-DA) was developed to discriminate between salt and MP spectra and subsequently to typify the polymeric nature of the detected MPs. Four of the most prevalent polymers in the Mediterranean Sea (polyethylene (PE), polyethylene terephthalate (PET), polystyrene (PS), and polyvinyl chloride (PVC)) were selected as reference standards. The workflow was first optimised and validated using reference and simulated salts and then applied to real sea salt samples collected from Mediterranean coastal saltworks and commercial grocery salts. The results demonstrated excellent classification performance, with 100 % sensitivity, specificity, and precision in both validation stages. Among the analysed samples, MP contamination was confirmed in 3 coastal and 2 commercial salts, with PET and PE being the dominant polymers. These findings highlight sea salt as a valuable proxy for marine MP contamination and as a potential route of human exposure. Overall, this study introduces a green, efficient, and reproducible analytical approach for MPs detection and typification, providing a foundation for future large-scale environmental monitoring and risk assessment initiatives.
MedinaGarcía et al. (Mon,) studied this question.