Digital Identification of Angelicae Sinensis Radix and Angelicae Pubescentis Radix
Objective:To explore a method for the digital identification and analysis of Angelicae Sinensis Radix(ASR)and Angelicae Pubescentis Radix(APR)based on ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UPLC-QTOF-MS)analysis and quantization processing.Methods:UPLC-QTOF-MS was utilized to analyze ASR and APR.Progenesis QI software was used for peak correction and extraction,converting the mass spectra of ASR and APR into data matrices(retention time-mass to charge ratio-ionic intensity).Feature screening was further conducted based on information gain and information gain ratio.Data identification models were then established using artificial neural networks(ANNs),support vector machines(SVM),logistic regression(LR),and K-nearest neighbors(KNN)machine learning algorithms.Cross-validation and model analysis were employed to select the best model for the digital identification and analysis of ASR and APR.Results:A total of 603 feature data variables were obtained through feature screening.Compared with the SVM,LR,and KNN algorithm models,the identification model constructed with the screened feature data and ANNs algorithm demonstrated the best recognition effect,with both accuracy and precision rates of 100%and an area under the ROC curve of 1.000.External validation confirmed that the model could accurately identify ASR and APR.Conclusion:The digital identification of ASR and APR can be efficiently and accurately achieved based on UPLC-QTOF-MS quantized data combined with the ANNs algorithm.This method can provide a reference for the digital identification and analysis of Chinese medicine.
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