Studies On Chemical Constituents And Quality Standard Of Inula Cappa

Inula cappa,belonging to the genus Inula of family Asteraceae,is the dried whole plant,which is one of the main components in Ya-Jiao-Ha-Dun San of a famous Dai medicine.Due to the effectiveness of evacuation of wind cold and detumescence,Inula cappa,containing sesuiterpene,triterpene,organic acid,sterol,flavonoid,and acetanilide,has been widely used for the treatment of cold and fever,rheumatism,sore throat,etc.Inula cappa has not been adopted by the Chinese pharmacopea and local standards were not perfect.In this study,the chemical constituents and quality control of Inula cappa were systematically studied to provide theoretical proof and practical reference for its clinical application and futher research.1.Chemical constituentsIn this study,by extraction,separation,purification,and identification,seven constituents were isolated from EAC extract of Inula cappa,which were ethyl caffetae(1),daucosterol(2),?-sitosterol(3),apigenin(4),3-(4-hydroxy-3-methoxyphenyl)-propenal(5),palmitic acid(6),and 5-hydroxymethyl furfural(7).The constituent 1 was firstly isolated from Inula cappa.2.Quality standard2.1 By established the method of TLC identification using scopoletin as standard,TLC identification of Inula cappa was improver.2.2 According to the general rule 0832?2302?2201 of Chinese pharmacopoeia of 2015 edition,the moisture,ash,and enthanol extract of Inula cappa were determined respectively.2.3 By established the HPLC method of quantitative analysis of multi-components by single marker(QAMS),the contents of 1,3-O-dicaffeoylquinic acid,3,4-O-dicaffeoylquinic acid,3,5-O-dicaffeoylquinic acid(isochlorogenic acid A),1,5-O-dicaffeoylquinic acid,and 4,5-O-dicaffeoylquinic acid in the Inula cappa were determined using 3,5-O-dicaffeoylquinic acid as the standard.3.HPLC fingerprintBy established the HPLC fingerprint of Inula cappa and selected 22 characteristic peaks,the sililarity of 15 samples were above 0.900.The principle component analysis(PCA)and clustering analysis(CA)showed that 15 batches of samples were divided into two classes.