标题：Comparison, artificial neural network modeling and genetic algorithm optimization of the resinoid and potassium yields from white lady’s bedstraw (Galium mollugo L.) by conventional, reflux and ultrasound-assisted aqueous-ethanolic extraction
期刊名称：Chemical Industry and Chemical Engineering Quarterly
印刷版ISSN：1451-9372
出版年度：2013
卷号：19
期号：1
页码：141-152
DOI：10.2298/CICEQ120316049M
出版社：Association of the Chemical Engineers
摘要：In this work, the yields of resinoid and potassium obtained from aerial parts of white lady’s bedstraw (Galium mollugo L.) by maceration, reflux extraction and ultrasound-assisted extraction using aqueous ethanol solutions as solvents. The main goal was to define the influence of the extraction technique and the ethanol concentration on the resinoid and potassium yields. The resinoid and potassium yields were determined by the solvent evaporation from the liquid extracts to constant weight and the AAS emission method, respectively. The dependence of resinoid and potassium yields on the ethanol concentration was described by linear and quadratic polynomial models, respectively. The best potassium extraction selectivity of 0.077 g K/g of dry extract was achieved by maceration at the ethanol concentrations of 10 g/100 g. The artificial neural network (ANN) was successfully applied to estimate the resinoid and potassium yields based on the ethanol concentration in the extracting solvent and the time duration for all three extraction techniques employed. The response surface methodology was also used to present the dependence of ANN results on the operating factors. The extraction process was optimized using the ANN model coupled with genetic algorithm. The maximum predicted resinoid and potassium yields of 30.4 and 1.67 g/100 g of dry plant were obtained by the ultrasonic extraction (80 min) using the 10 g/100 g aqueous ethanol solution.
