Coupling hyperspectral imaging with machine learning algorithms for detecting polyethylene (PE) and polyamide (PA) in soils.

Soil, particularly in agricultural regions, has been recognized as one of the significant reservoirs for the emerging contaminant of MPs. Therefore, developing a rapid and efficient method is critical for their identification in soil. Here, we coupled HSI systems [i.e., VNIR (400-1000 nm), InGaAs (800-1600 nm), and MCT (1000-2500 nm)] with machine learning algorithms to distinguish soils spiked with white PE and PA (average size of 50 and 300 µm, respectively). The soil-normalized SWIR spectra unveiled significant spectral differences not only between control soil and pure MPs (i.e., PE 100% and PA 100%) but also among five soil-MPs mixtures (i.e., PE 1.6%, PE 6.9%, PA 5.0%, and PA 11.3%). This was primarily attributable to the 1st-3rd overtones and combination bands of C-H groups in MPs. Feature reductions visually demonstrated the separability of seven sample types by SWIR and the inseparability of five soil-MPs mixtures by VNIR. The detection models achieved higher accuracies using InGaAs (92-100%) and MCT (97-100%) compared to VNIR (44-87%), classifying 7 sample types. Our study indicated the feasibility of InGaAs and MCT HSI systems in detecting PE (as low as 1.6%) and PA (as low as 5.0%) in soil. SYNOPSIS: One of two SWIR HSI systems (i.e., InGaAs and MCT) with a sample imaging surface area of 3.6 mm² per grid cell was sufficient for detecting PE (as low as 1.6%) and PA (as low as 5.0%) in soils without the digestion and separation procedures.