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This review systematically explores the application of machine learning technology in the field of microplastics, covering classification and identification, quantitative analysis, and prediction of adsorption properties. By combing through recent literature, it has been found that technologies such as convolutional neural networks (CNN) and support vector machines (SVM) are of great significance for improving the accuracy and efficiency of microplastic detection. In classification and identification, CNN models can accurately distinguish the types and shapes of microplastics; during quantitative analysis, machine learning can quickly determine the concentration of microplastics with the help of image and spectral data. In terms of predicting adsorption properties, models based on quantitative structure-property relationships (QSPR) have shown higher accuracy and robustness than traditional models. However, there are currently challenges such as poor data quality, difficulties in collection and annotation, and a lack of model interpretability. Future research should focus on diversifying datasets and enhancing model interpretability to promote the further application of machine learning technology in microplastic research.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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