Objective:To develop an HPLC gradient elution method for the simultaneous determination of fangchinoline,tetrandrine,mesaconitine,aconitine and hypaconitine in Huoxue Zhentong plaster.Methods::A Dikma-C18 (200 mm×4.6 mm,5 μm) chromatographic column was adopted,the mobile phase was methanol-acetonitrile (3∶1)(A)-0.06% diaethylamin solution (B) with gradient elution at a flow rate of 1.0 ml·min-1,the detection wavelength was 280 nm for fangchinoline and tetrandrine,and 235 nm for mesaconitine,aconitine and hypaconitine.The column temperature was set at 30 ℃,and the injection volume was 10 μl.Results::There was a good linear relationship when the content of fangchinoline,tetrandrine,mesaconitine,aconitine and hypaconitine was within the range of 7.490-149.800 μg·ml-1(r=0.999 9),14.610-292.200 μg·ml-1(r=0.999 8),4.150-83.000 μg·ml-1(r=0.999 2),5.250-105.000 μg·ml-1(r=0.999 6) and 5.140-102.800 μg·ml-1(r=0.999 9),respectively.The average recovery and the corresponding RSD were 99.87%(0.49%),97.79%(1.11%),96.97%(1.75%),98.60%(1.50%) and 97.94%(0.98%)(n=6),respectively.Conclusion:An HPLC gradient elution method is successfully established for the simultaneous determination of 5 components in Huoxue Zhentong plaster.The established method is simple,accurate and reliable,which is helpful to the quality control of Huoxue Zhentong plaster.

OBJECTIVE:To compare the performance of several pattern recognition methods in the identification of volatile oils of traditional Chinese medicine(TCM)by infrared spectroscopy. METHODS:The volatile oils of several Lonicera and Citrus TCM were determined by infrared spectroscopy. All samples of infrared spectrum were classified by hierarchical clustering,K-mean clustering,artificial neural networks,and support vector machine. RESULTS:The results of hierarchical clustering and K-mean clus-tering were ineffective. Methods of artificial neural networks and support vector machine achieved correct classification rate of 100%. CONCLUSIONS:Artificial neural networks and support vector machine can be combined with infrared spectroscopy to cre-ate chemometric fingerprinting for the identification of volatile oils of TCM.

Objective To set up a method for identification of Lonicerae japonicae flos volatile oils using Fourier-transform infrared spectroscopy. Methods The volatile oils of Lonicerae japonicae flos and Lonicerae flos was extracted by steam distillation combined with continuous liquid-liquid extraction with hexane. An oil film was prepared for Fourier-transform infrared spectroscopy scanning by dropping the volatile oils solution on the KBr disc and evaporating the solvent. The obtained infrared spectrum was treated by baseline removing and median filter smoothing. The spectral data within 1800-850 cm-1 was selected as the characteristic spectrum for hierarchical cluster analysis. And the volatile oils of Lonicerae japonicae flos and Lonicerae flos were discriminated by the result of hierarchical cluster analysis. Results Enough volatile oils were extracted for obtaining Fourier-transform infrared spectrum from small amount of Lonicerae japonicae flos. The method developed in the study was able to discriminate Lonicerae japonicae flos volatile oils from Lonicerae flos volatile oils. Conclusion The method can be used for identification of Lonicerae japonicae flos volatile oils.