A Study Guided by Drug Regulatory Philosophy on the Authenticity Discrimination of Artemisiae Argyi Fo-lium Based on the Combination of Near-Infrared Spectroscopy and Machine Learning
A Study Guided by Drug Regulatory Philosophy on the Authenticity Discrimination of Artemisiae Argyi Fo-lium Based on the Combination of Near-Infrared Spectroscopy and Machine Learning
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 discrimina-tion accuracy of the samples in the test set was 97％,and the other evaluation indicators were also above 92％with the logistic regres-sion 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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万方数据