OBJECTIVE To optimize the supercritical extraction process for the active components in Curcuma phaeocaulis valeton with back-propagation neural network and genetic algorithm.METHODS Gas chromatography was used to determine the contents of curcumol in the extract.BP neural network was established and optimized with genetic algorithm to forecast the supercritical extraction of Curcuma phaeocaulis valeton.Uniform design and genetic algorithm were used to optimize the trained BP network to obtain optimum SFE process.RESULTS The optimum process was established as follows:20 MPa as extracting pressure,45 ℃ as extracting temperature,80 min as dynamic extracting time and 27 min as static extracting time,25 mL of 72% alcohol as modifier.The relative error between the predicted value from BP network and observed value was lower than 4%.CONCLUSION GA-optimized BP neural network can be employed to forecast SFE extraction of active components in Chinese herb medicines.GA-optimized SFE processes are better than the process optimized by nonlinear regress.
LIU Hong-mei;LI Ke-yi.
Study on Supercritical Extraction Process of Curcumol in Curcuma phaeocaulis Valeton Based on Back-Propagation Neural Network and Genetic Algorithm [J]. Chinese Pharmaceutical Journal, 2006, 41(05): 371-374
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