High-speed counter-current chromatography (HSCCC) was applied to the preparative isolation and purification of peonidin 3-O-(6-O-(E)-caffeoyl-2-O-beta-D-glucopyranosyl-beta-D-glucopyranoside)-5-O-beta-D-glucoside (1), cyanidin 3-O-(6-O-p-coumaroyl)-beta-D-glucopyranoside (2), peonidin 3-O-(2-O-(6-O-(E)-caffeoyl-beta-D-glucopyranosyl)-6-O-(E)-caffeoyl-beta-D-glucopyranoside)-5-O-beta-D-glucopyranoside (3), peonidin 3-O-(2-O-(6-O-(E)-feruloyl-beta-D-glucopyranosyl)-6-O-(E)-caffeoyl-beta-D-glucopyranoside)-5-O-beta-D-glucopyranoside (4) from purple sweet potato. Separation of crude extracts (200 mg) from the roots of purple sweet potato using methyl tert-butyl ether/n-butanol/acetonitrile/water/trifluoroacetic acid (1:4:1:5:0.01, v/v) as the two-phase solvent system yielded 1 (15 mg), 2 (7 mg), 3 (10 mg), and 4 (12 mg). The purities of 1-4 were 95.5%, 95.0%, 97.8%, and 96.3%, respectively, as determined by HPLC. Compound 2 was isolated from purple sweet potato for the first time. The chemical structures of these components were identified by 1H NMR, 13C NMR and ESI-MS(n).
Partial discharge (PD) is usually aroused before the failure of gas-insulated switchgear (GIS) caused by insulation defects, which results in the decomposition of SF 6 used as insulation gas. Concentrations of decomposition products of SF 6 under different kinds of PDs are disparate. Thus, SF 6 decomposition products can be used for PD recognition. In this study, a gas chamber and four defect models were designed to simulate four kinds of typical PDs in GIS. SF 6 decomposition experiments were conducted under the four kinds of PDs. Four kinds of decomposition products, that is, SO 2 F 2 , SOF 2 , CO 2 and CF 4 , were selected as feature components. Their concentrations were detected under each experiment. Three concentration ratios, that is, c(SO 2 F 2 )/c(SOF 2 ), c(CF 4 )/c(CO 2 ) and c(CO 2 + CF 4 )/c(SOF 2 + SO 2 F 2 ), were proposed as feature parameters for PD recognition. Their physical significances were also analysed. Then, a support vector machine (SVM) was employed as classifier to recognise the four kinds of PDs. The parameters of the SVM were optimised using particle swarm optimisation algorithm. Results show that the recognition method based on SF 6 decomposition products and SVM performs well in PD recognition.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.