Objective: To study the specificity of the determination method for catechin and epicatechin in Catechu. Method: Contents of catechin and epicatechin in 7 kinds of crude drugs and 3 kinds of preparations of Catechu were determined. Results: As sampling 100 times as much as Catechu, catechin and epicatechin could be both detected in Semen Arecae, and catechin could be detected in Radix et Rhizoma Rhei, Herba Ephedrae and Radix Sanguisorbae. Conclusion:This method can be used for the quality control of Catechu and its preparations and is of specificity.

Objective To set up a clean-up method using gel permeation chromatography(GPC)and ENVI-Carb-SPE.The residues of triadimefon and its metablites,triadimenol A and triadimenol B in ginseng were detected by GC-MS with negative chemical ionization(NCI).Methods The sample was extracted with acetone and the extract was cleaned using GPC and ENVI-Carb-SPE.Based on GC-MS(NCI)the pesticides were separated on a DB-5MS column using a temperature program and were detected with a mass selective detector in selective ion monitoring(SIM)mode.The reference solution was prepared by the blank sample extract to overcome the matrix effect,the external reference method was used to detect.Results Three pesticides were separated within 10 min.The average spiked recoveries in three levels were 90%—105% with relative standard deviations(RSD)below 6%(n=6)in roots and stems.The limits of detection(LOD)of triadimefon and triadimenols were 0.1 and 10 ?g/L.The precision was below 2%(n=6).Conclusion The method is sensitive for the residue analysis of three pesticides and could be used to the triadimefon and triadimenols detection and security control in ginseng.

Objective To establish a reversed -phase HPLC m ethod for the determination of psora len and isopsoralen in Fukangbao capsule.Methods The contents of Psoralen and Isopsoralen were assayed on a ODS -C 18 column with a mobile phase of methanol -water(40∶60)at a column temperature of 35℃.The fl ow rate was 1.0mL /min.The detection wave-length was at 247nm.Results The linear ranges of Psoralen and Iso psoralen were 1.05～10.52?g /mL(r =0.9999)and1.02～10.20?g /mL(r =0.9999)respectively.Their recoveries were within 97.5%(Psoralen)and 100.8%(Isopsoralen),and both of their RSD were 0.6%.Conclusion This method is simple,rapid and accu rate and suitable for quantity -limiting control of Fukan gbao capsule.

In order to clarify original plants of traditional Chinese medicine (TCM) frankincense, a GC method for determination essential oils and a HPLC method for determination boswellic acids were carried out together with analysis of herbalism, botany, components and pharmacology papers of frankincense. It was concluded that original plants of TCM frankincense include at least Boswellia sacra, B. papyrifera and B. serrata.
Boswellia ; chemistry ; classification ; Chromatography, High Pressure Liquid ; Herbal Medicine ; Plant Extracts ; analysis

ObjectiveThis study aims to develop a prediction model for the compatibility of Chinese medicinal pairs based on Graph Convolutional Networks （GCN）， named HC-GCN. The model integrates the properties of herbs with modern pharmacological mechanisms to predict pairs with specific therapeutic effects. It serves as a demonstration by applying the model to predict and validate the efficacy of blood-activating herb pairs. MethodsThe training dataset for herb pair prediction was constructed by systematically collecting commonly used herb pairs along with their characteristic data， including Qi， flavor， meridian tropism， and target genes. Integrating traditional characteristics of herb with modern bioinformatics， we developed an efficacy-oriented herb pair compatibility prediction model （HC-GCN） using graph convolutional networks （GCN）. This model leverages machine learning to capture the complex relationships in herb pair compatibility， weighted by efficacy features. The performance of the HC-GCN model was evaluated using accuracy （ACC）， recall， precision， F1 score （F1）， and area under the ROC curve （AUC）. Its predictive effectiveness was then compared to five other machine learning models： eXtreme Gradient Boosting （XGBoost）， logistic regression （LR）， Naive Bayes， K-nearest neighbor （KNN）， and support vector machine （SVM）. ResultsUsing herb pairs with blood-activating effects as a demonstration， a prediction model was constructed based on a foundational dataset of 46 blood-activating herb pairs， incorporating their Qi， flavor， meridian tropism， and target gene characteristics. The HC-GCN model outperforms other commonly used machine learning models in key performance metrics， including ACC， recall， precision， F1 score， and AUC. Through the predictive analysis of the HC-GCN model， 60 herb pairs with blood-activating effects were successfully identified. Among of these potential herb pairs， 44 include at least one herb with blood-activating effects. ConclusionIn this study， we established an efficacy-oriented compatibility prediction model for herb pairs based on GCN by integrating the unique characteristics of traditional herbs with modern pharmacological mechanisms. This model demonstrated high predictive performance， offering a novel approach for the intelligent screening and optimization of traditional Chinese medicine prescriptions， as well as their clinical applications.