Study On Chemical Compositions Of Jingtiao Propolis And Analytical Methods For Antidiabetic Drugs

This paper was carried out to investigate the chemical compositions of Jingtiao propolis. Propolis raw material was extracted by65%ethanol at80℃. The extract was subjected to concentration under reduced pressure, solvent partition, silica gel column chromatography, recrystallization and high preparative performance liquid chromatography. Thirty one compounds were obtained and their structures were identified on the basis of the data of physical and chemical properties test, IR, UV, MS and NMR analysis. Fourteen of which were flavanoids, seven were caffeic acid analogues, six were caffeic esters, and four are other categories.Propolis extraction by supercritical CO2was improved with evening primrose oil added as co-solvent. When the ratio of propolis and evening primrose oil is6to1, the extraction rate of propolis and the extraction rate of flavonoids would be reached to13.8%and18.8%, respectively. The propolis extract was golden-colored and pure. Compared with the result of ethanol added as co-solvent, the propolis yield was maintained and status of appearance and taste of extract were also promoted. As a healthy food, evening primrose oil was needn’t to be separated after extraction, which made this method possible to be an approach for new compound type healthy food preparation.In this study, the separation and identification of polyphenol compounds from Chinese propolis samples were investigated using off-line two-dimensional liquid chromatography (2D-LC) in combination with Q-TOF-MS. The Click OEG (ethylene glycol bonding stationary phase) and XTerra C18column were employed for the first and second dimensional HPLC separation, respectively. The compounds were allowed to be identified by matching their retention times, accurate mass molecular weights and MS/MS data, and81components were characterized from the samples from Henan, Zhejiang and Liaoning Province. By contrast, only36peaks were detected by the1D-LC method. The2D-LC/MS/MS method was demonstrated to be more efficient for the existence or absence of target compounds identification, especially co-eluted compounds or minor compounds from complex samples.A fast, simple and accurate near infrared spectroscopy (NIR) method was investigated for the geographical origin discrimination and eight main flavonoids content determination of Chinese propolis. A principal component analysis (PCA)-Mahalanobis distance (MD) method was applied to classify the geographical origin of propolis samples. All the S were larger than1, and100%accuracy was obtained. The partial least squares (PLS) models of eight and total flavonoids were established. The root mean square error of prediction (RMSEP) and correlation coefficient (R2) in test set of the optimum models were less than1.33%and larger than76.23%. Compared to HPLC method, NIR was accurate and could shorten determination period from70min to3min with no troublesome sample preparation. Especially the usage of contactless fiber-coupled measurement head enhanced the feasibility on on-line quality control of propolis.A new method for fast determination of glibenclamide in Xiaoke pills using NIR method. The multivariate calibration model based on PLS algorithm was developed to correlate the spectra and the corresponding values were determined by the reference method. In the range of7502.3-4597.8cm-1, the R2and root mean square error of cross-validation (RMSECV) in calibration set were99.86%and3.59%, respectively. The RMSEP in prediction set was5.67%(less than10%). This new method was fast, accurate and easy-operating, which could be extended to fast quality control of the Xiaoke pills production. Meanwhile, a rapid, accurate and no sample preparation required method to identify8kinds of oral hypoglycemic active pharmaceutical ingredient (API)(solid and liquid) by NIR was established. A solid fiber coupled measurement head was used to collect spectra, and first derivative combined with scaling to1st range method was conducted as the optimum pre-processing method at4000.0-12072.3cm-1. With this approach,100%discrimination rate was obtained for fast identification of eight oral hypoglycemic APIs.