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OBJECTIVE To establish a method for identifying the authenticity of Artemisiae Argyi Folium suitable for use in drug regulatory work. METHODS The near-infrared spectra of samples of Artemisiae Argyi Folium and counterfeit were determined, and the experimental data was processed using feature engineering related techniques, such as feature screening and feature derivation. The training set and test set were divided randomly, and the logistic regression model, a classic model in the field of machine learning, was trained in 2-class mode and evaluated with the training set data and the test set data used, respectively. RESULTS The discrimination accuracy of the samples in the test set was 97%, and the other evaluation indicators were also above 92% with the logistic regression model. In addition, the results of discrimination between genuine and counterfeit mixed samples were also relatively accurate. Compared with traditional chemometrics methods, the machine learning used in the study had higher discrimination accuracy. CONCLUSION The logistic regression model established in this study can achieve the authenticity identification of Artemisiae Argyi Folium, providing technical support for actual drug regulatory work.
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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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