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This study conducted X-ray fluorescence (XRF) and X-ray diffraction (XRD) experiments on 10 common types of solid waste. By employing non-parametric tests, the XRF and XRD fingerprint characteristics of solid waste were extracted. A normalized approach was adopted to combine the two fingerprint characteristics to form XRF-XRD coupled spectral fingerprint characteristics. A machine learning classification model was trained using the three fingerprint characteristics as a dataset. It was found that the random forest model exhibited the best classification recognition performance, with classification accuracy, recall rate, and precision rate in the validation set reaching 98.0%, 97.5%, and 97.6%, respectively. This confirmed that the classification performance of coupled spectral fingerprint characteristics was superior to that of single spectral fingerprint characteristics. A method for extracting multi-dimensional fingerprint characteristics of solid waste using XRF-XRD was developed, laying a methodological foundation for the identification of fingerprint characteristics of solid waste.
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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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