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Near-infrared (NIR) spectroscopy for quantitative modelling of quaternary microplastic mixtures and the effect of interferents
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Scientists tested a portable scanning device that uses infrared light to quickly identify and measure different types of plastic pollution (like the kind found in food packaging and synthetic fabrics) even when mixed with real-world debris like dirt, sand, and plant matter. The tool worked well, meaning researchers could soon have a faster, cheaper way to track how much plastic is contaminating our environment. Since microplastics have been found in human blood, organs, and food, better detection tools like this are an important step toward understanding, and eventually reducing, our exposure to them.
The detection and quantification of microplastics (MPs) in environmental samples remain a significant analytical challenge due to the heterogeneity of polymer mixtures and the presence of organic and inorganic interferents. While Near-infrared (NIR) spectroscopy has emerged as a rapid, cost-effective alternative, most studies have focused on qualitative detection or simplified systems, leaving the influence of environmental interferents largely unexplored. This study proposes a quantitative analytical strategy using a portable NIR spectrometer combined with multivariate regression for the determination of four target polymers (polypropylene, PP, polyethylene, PE, polystyrene, PS, and polyethylene terephthalate, PET) in complex mixtures. MPs were generated through a true-to-life protocol, ensuring realistic particle morphologies and surface conditions. Model robustness was systematically assessed against a wide range of environmental interferents, including non-target polymers (polyvinyl chloride, polylactic acid, and polyamide), natural fibres (cotton, silk), vegetal material, and mineral particles (CaCO₃). Polymer quantification was performed through Partial Least Squares (PLS) regression, with each polymer modelled independently. The proposed modelling approach was subjected to a double cross-validation procedure, and their predictive ability was further estimated by external validation procedure. In particular, when external validation samples were spiked with interferents, prediction errors increased moderately due to added spectral complexity; however, the models maintained satisfactory performance, with PE and PET demonstrating the greatest resilience to matrix effects. Finally, the models were successfully applied for the quantification in real environmental samples, with a satisfactory accuracy considering the inherent complexity of “unknown” environmental matrices. These results demonstrate the potential of portable NIR spectroscopy and robust chemometric modelling for quantitative MP analysis in heterogeneous, environmentally realistic scenarios.
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