Hyperspectral imaging for detection and quantification of microplastics in soil: development of a comprehensive spectral library and a data analysis methodology

We present an optics-driven hyperspectral imaging (HSI) framework for the label-free detection and quantitative assessment of microplastic particles in soil matrices, operating in the visible-near-infrared (VIS-NIR, 400-950 nm) spectral range. By exploiting the size-dependent reflectance behavior of polymeric materials, a comprehensive spectral library was constructed for four widely used polymers- polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), and acrylonitrile butadiene styrene (ABS)- across multiple particle-size regimes. A custom spectral-difference (SD) algorithm was developed to isolate polymer-specific optical signatures from heterogeneous soil backgrounds at the pixel level, enabling non-destructive identification without the need for chemical labeling or complex chemometric models. Systematic hyperspectral measurements reveal distinct characteristic wavelengths and linear scaling of reflectance intensity with particle size, providing an optical basis for polymer discrimination and sensitivity analysis. Beyond qualitative identification, we establish a quantitative relationship between surface-sensitive hyperspectral signals and mass-based microplastic concentrations (ppm), demonstrating a calibration strategy that bridges optical observables and gravimetric metrics. Independent Raman spectroscopy and GC-MS analyses are employed solely as validation references for polymer identity and concentration. The proposed approach advances hyperspectral imaging from a qualitative screening tool toward a quantitative optical modality for microplastic analysis, offering a scalable pathway for high-throughput, non-invasive characterization of particulate contaminants in complex media.