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The presence of microplastics (MPs) in agricultural soils substantially affects the growth, reproduction, feeding, survival, and immunity levels of soil biota. Therefore, it is crucial to investigate fast, effective, and accurate techniques for the detection of soil MPs. This work explores the integration of terahertz time-domain spectroscopy (THz-TDS) techniques with machine learning algorithms to develop a method for the classification and detection of MPs. First, THz spectral image data were preprocessed using moving average (MA). Subsequently, three classification models were developed, including random forest (RF), linear discriminant analysis, and support vector machine (SVM). Notably, the SVM model had an F1 score of 0.9817, demonstrating its ability to rapidly classify MPs in soil samples. Three regression models, namely, principal component regression (PCR), RF, and least squares support vector machine (LSSVM), were developed for the detection of three MPs polymers in agricultural soils. Six feature extraction methods were used to extract the relevant parts of the data containing key information. The results of the study showed that the regression accuracies of PCR, RF, and LSSVM were greater than 83%. Among them, the RF had the highest overall regression accuracy. Notably, PE-UVE-RF had the best performance with R c 2, R p 2, root mean square error of calibration, and root mean square error of prediction values of 0.9974, 0.9916, 0.1595, and 0.2680, respectively. Furthermore, this model gets a better performance by hypothesis testing and predicting real samples.
Xu et al. (Fri,) studied this question.