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Soil Microplastics Spectrum Based on Visible Near-Infrared Spectroscopy
Summary
Researchers developed a visible near-infrared spectroscopy method for quantifying microplastics in soil, finding that spectral reflectance decreases with increasing microplastic content and that a regression model combining normalisation with first-derivative transformation achieved the best predictive accuracy with R-squared values of 0.75 and 0.77 for calibration and validation sets.
As the largest "repository" of microplastics, soil is affected by soil structure, microplastics category and particle size. In this study, the method of combining indoor simulation modeling and field verification is proposed. The reflectance of soil microplastic samples was collected by ASD FieldSpec4 Hi-Res spectrometer, NOR, MSC, SNV were used for spectral pretreatment, and differential transformations of different orders are used to enhance the spectral signal-to-noise ratio. The results showed that the spectral reflectance of microplastics decreased with the increase of microplastics content in soil. After FD and SD transform, the spectral features are enhanced obviously. The regression model based on NOR transformation of reflectivity combined with first deviation is the best, Rc2, Rv2, RMSEC and RMSEPare 0.75, 0.77, 0.16, 0.12,respectively.This study can provide a scientific basis for quantitative research on microplastics in farmland soil in northern Shaanxi, China. Bangladesh J. Bot. 51(4): 971-977, 2022 (December) Special