Optimization of extraction of linarin from Flos chrysanthemi indici by response surface methodology and artificial neural network.
The extraction of linarin from Flos chrysanthemi indici by ethanol was investigated. Two modeling techniques, response surface methodology and artificial neural network, were adopted to optimize the process parameters, such as, ethanol concentration, extraction period, extraction frequency, and solvent to material ratio. We showed that both methods provided good predictions, but artificial neural network provided a better and more accurate result. The optimum process parameters include, ethanol concentration of 74%, extraction period of 2 h, extraction three times, solvent to material ratio of 12 mL/g. The experiment yield of linarin was 90.5% that deviated less than 1.6% from that obtained by predicted result.
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, P. R. China.
Hongye
H
Pan
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, P. R. China.
Qing
Q
Zhang
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, P. R. China.
Keke
K
Cui
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, P. R. China.
Guoquan
G
Chen
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, P. R. China.
Xuesong
X
Liu
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, P. R. China.
Longhu
http://orcid.org/0000-0001-8843-7263
L
Wang
D031202:Chrysanthemum / Q000737:chemistry*; D035264:Flowers / Q000737:chemistry; D006027:Glycosides / Q000302:isolation & purification*; D016571:Neural Networks, Computer
D006027:Glycosides; C008282:linarin
artificial neural networks; linarin; response surface methodology