ABSTRACT
Objective:To compare volatile components in raw products and steamed products of Citri Sarcodactylis Fructus. Method:Volatile oil in different batch of raw products and steamed products of Citri Sarcodactylis Fructus was extracted by steam distillation, and volatile components were identified by GC-MS.The determination was performed with injector temperature of 280℃, high pure helium(≥ 99.999%) as carrier gas, split ratio of 10:1, and sample size of 3 μL with temperature programming.Mass spectrum conditions included electron bombardment ion source, electron bombardment energy of 70 eV, ion source temperature of 230℃, acceleration voltage of 34.6 V, resolution of 2 500, scanning range of m/z 40-350.Peak matching, cutting and noise filtering were used in analyzing data based on GC-MS combined with orthogonal partial least square-discriminant analysis(OPLS-DA). Result:A total of 58 components were separated and identified from the different batches of raw Citri Sarcodactylis Fructus and their processed products, and 27 differential chemical components were identified by multivariate statistical analysis, among them, 15 common differential components presented different changing laws.The relative contents of α-pinene, β-pinene, p-cymene, β-linalool, L-4-terpinenol, α-terpineol, nerol, β-cyclocitral, geraniol and α-citral in raw products were higher than that of their corresponding processed products.The relative contents of β-caryophyllene, cis-α-bergamotene, γ-muurolene, γ-elemene and β-bisabolene in processed products were higher than that of their corresponding raw products. Conclusion:The content and composition of volatile components in Citri Sarcodactylis Fructus from Guangdong province have changed after being steamed.The results can provide experimental basis for expounding the processing principle and quality evaluation of Citri Sarcodactylis Fructus, and have positive significance for promoting the research and development of this Lingnan characteristic decoction pieces.
ABSTRACT
Objective: To evaluate the effect of manual decocting and machine decocting on the chemical constituents in Bazhentang based on non-targeted metabolomics, and to find the differential chemical constituents of these two decocting methods. Method: Bazhentang was boiled by standardized manual decocting and machine decocting methods,respectively. Orthogonal partial least square-discriminate analysis(OPLS-DA) and other multivariate statistical methods, combined with variable importance in the projection(VIP) value and t-test, were employed to analyze the effect of two decocting methods on the chemical constituents in Bazhentang. The differential chemical constituents were analyzed by UPLC-LTQ-Orbitrap MS under positive and negative ion modes,mobile phase was acetonitrile-0.1%formic acid aqueous solution for gradient elution,the scanning range was m/z 50-1 500. Result: Under the positive and negative ion modes of high-resolution mass spectrometry, a total of 87 differential components were found,40 of them were identified according to the mass spectrometry data and literature reports, including senkyunolide A, glycyrrhizin, ferulic acid, etc. Conclusion: Based on the analysis of color and chemical compositions of Bazhentang, there are obvious differences between the standardized manual decocting and machine decocting. If the advantages of these two methods are combined,a standardized decoction process can be established on the basis of maintaining the advantages of manual decocting, the effectiveness of traditional Chinese medicine decoction will be maximized and it will be convenient for patients to take it.
ABSTRACT
Objective:To establish an effective classification and identification method for sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) maps of Rana dybowskii,its analogues and counterfeits based on cluster analysis and multivariate statistical analysis. Method:SDS-PAGE maps of 18 batches of R. dybowskii,its analogues and 2 counterfeits were obtained by SDS-PAGE method. SDS-PAGE maps were transformed into data matrix. NTSYSpc 2.10e statistical analysis software was used for cluster analysis,and SMICA-P 14.1 software was used for multivariate statistical analysis. Unsupervised Principal Component Analysis (PCA),Supervised Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) were performed for multivariate analysis and evaluation. Result:SDS-PAGE maps technology combined with cluster analysis and multivariate statistical analysis could accurately classify and identify R. dybowskii,its analogues and counterfeits. Cluster analysis could cluster four kinds of medicinal materials into four branches except No.1 medicinal materials. PCA results were superior to cluster analysis. Supervised PLS-DA and OPLS-DA results in multivariate statistical analysis were superior to unsupervised PCA. The classification and identification efficiencies of OPLS-DA were better than those of unsupervised PCA. OPLS-DA aggregated R. dybowskii,its analogues and 2 counterfeits into four groups. Six different protein components were obtained by comprehensive analysis of variable importance in projection (VIP) value, and OPLS-DA Bi load diagram,with relative molecular weights were 51.363,35.838,14.565,17.563,15.358 and 21.696 kDa,respectively. Conclusion:SDS-PAGE maps combined with cluster analysis and multivariate statistical analysis can be used as an effective method to classify and identify R. dybowskii,its analogues and counterfeits. This study provides a reference for the quality evaluation and screening of R. dybowskii.