Prediction of the extraction yield using artificial neural network and response surface methodology: ultrasound-assisted extraction from Achillea berbresteinii L.

Main Article Content

D. Salarbashi
F. Khanzadeh
S.M. Hosseini
M. Mohamadi
A. Rajaei
A. Daraei Garmakhany


Achillea L, artificial neural network-genetic algorithm, phenolic compounds, response surface methodology


This study investigates the extraction efficiency of phenolic compounds from Achillea berbresteinii by ultrasound-assisted extraction (UAE) method. Meanwhile, to predict the phenolic compound extraction yield, artificial neural network-genetic algorithm (ANN-GA) and response surface methodology (RSM) were compared. The results indicated that UAE method could significantly improve the extraction yield in comparison to conventional method. Optimised processing conditions were 35 °C, 6.3, 20% and 35 min as temperature, pH, solvent to sample ratio and extraction time, respectively. On the other hand, hybrid ANN-GA was employed to estimate the phenolic compound extraction yield. The results revealed the better capability of this method in comparison to RSM. Optimised network contained 8 and 3 neurons in first and second hidden layers, respectively. This configuration could estimate phenolic compound extraction yield with high correlation coefficient (0.94). Finally, A. berbresteinii can be considered as an excellent potential source of phenolic compounds and ANN-GA as a successful applied method for the prediction of the phenolic compound extraction yield.

Abstract 63 | PDF Downloads 31


Aghajani, N., Kashaninejad, M., Dehghani, A.A. and Daraei Garmakhany. A., 2012. Comparison between artificial neural networks and mathematical models for moisture ratio estimation in two varieties of green malt. Quality Assurance and Safety of Crops & Foods 4: 93-101.
Candan, F., Unlu, M., Tepe, B., Daferera, D., Polissiou, M., Sokmen, A. and Akpulat, A., 2003. Antioxidant and antimicrobial activity of the essential oil and methanol extracts of Achillea millefolium subsp. millefolium Afan. (Asteraceae). Journal of Ethnopharmacology 87: 215-220.
Dimitrios, D., 2006. Sources of natural phenolic antioxidants. Trends in Food Science & Technology 17: 505-512.
Heckerling, P.S., Gerber, B.S., Tape, T.G. and Wigton, R.S., 2004. Use of genetic algorithms for neural networks to predict community-acquired pneumonia. Artificial Intelligence in Medicine 30: 71-84.
Heydari Majd, M., Rajaei, A., Salar Bashi, D., Mortazavi, S.A. and Bolourian, S., 2014. Optimization of ultrasonic-assisted extraction of phenolic compounds from bovine pennyroyal (Phlomidoschema parviflorum) leaves using response surface methodology. Industrial Crops and Products 57: 195-202.
Hu, Q., Pan, B., Xu, J., Sheng, J. and Shi, Y., 2007. Effects of supercritical carbon dioxide extraction conditions on yields and antioxidant activity of Chlorella pyrenoidosa extracts. Journal of Food Engineering 80: 997-1001.
Izadifar, M. and Jahromi, M.Z., 2007. Application of genetic algorithm for optimization of vegetable oil hydrogenation process. Journal of Food Engineering 78: 1-8.
Jacques, R.A., Freitas, L.D.S., Perez, V.F., Dariva, C., Oliveria, A.P.D., Olivera, J.V.D. and Caramao, E.B., 2007. The use of ultrasound in the extraction of Ilex paraguariensis leaves: a comparison with maceration. Ultrasonics Sonochemistry 14: 6-12.
Kashiri, M., Daraei Garmakhany, A. and Deghani, A.A., 2012. Modeling of sorghum soaking using artificial neural networks (MLP). Quality Assurance and Safety of Crops & Foods 4: 179-184.
Konyalioglu, S. and Karamenderes, C., 2005. The protective effects of Achillea L. species native in Turkey against H2O2-induced oxidative damage in human erythrocytes and leucocytes. Journal of Ethnopharmacology 102: 221-227.
Lapornik, B., Prosek, M. and Wondra, A.G., 2005. Comparison of extracts prepared from plant by-products using different solvents and extraction time. Journal of Food Engineering 71: 214-222.
Luque-Garcia, J.L. and Luque de Castro, M.D., 2003. Ultrasound: a powerful tool for leaching. Trends in Analytical Chemistry 22: 41-47.
Mohebbi, A., Taheri, M. and Soltani, A., 2008a. A neural network for predicting saturated liquid density using genetic algorithm for pure and mixed refrigerants. International Journal of Refrigeration 31: 1317-1327.
Mohebbi, M., Barouei, J., Akbarzadeh-T, M.R., Rowhanimanesh, A.R., Habibi-Najafi, M.B. and Yavarmanesh, M., 2008b. Modeling and optimization of viscosity in enzyme-modified cheese by fuzzy logic and genetic algorithm. Computers and Electronics in Agriculture 62: 260-265.
Mokhtarian, M., Heydari Majd, M., Koushki, F., Bakhshabadi, H., Daraei Garmakhany, A. and, Rashidzadeh, Sh., 2014. Optimization of pumpkin mass transfer kinetic and predict final moisture content by ANN and RSM modeling. Quality Assurance and Safety of Crops & Foods 6: 201-214.
Morimoto, T., 2006. Genetic algorithm. In: Sablani, S.S., Datta, A.K., Rehman, M.S. and Mujumdar, A.S. (ed.). Handbook of food and bioprocess modeling techniques. CRC press, New York, NY, USA.
Rahimmalek, M., Tabatabaei, B.E.S., Arzani, A. and Etemadi, N., 2009. Assessment of genetic diversity among and within Achillea species using amplified fragment length polymorphism (AFLP). Biochemical Systematics and Ecology 37: 354-361.
Rostagno, M.A., Palma, M. and Barroso, C.G., 2008. Ultrasound assisted extraction of soy isoflavones. Journal of Chromatography A 1012: 119-128.
Shahabi Ghahfarrokhi, I., Daraei Garmakhany, A., Kashaninejad, M. and Dehghani, A. A., 2012. Estimation of peroxidase activity in red cabbage by artificial neural network. Quality Assurance and Safety of Crops & Foods 5: 163-167.
Salarbashi, D., Fazly Bazzaz, B. S., Karimkhani, M. M., Sabeti Noghabi, Z., Khanzadeh, F. and Sahebkar, A., 2014. Oil stability index and biological activities of Achillea biebersteinii and Achillea wilhelmsiiextracts as influenced by various ultrasound intensities. Industrial Crops and Products 55: 163-172.
Spigno, G. and Marco de Faveri, D., 2007. Antioxidants from grape stalks and marc: influence of extraction procedure on yield, purity and antioxidant power of the extracts. Journal of Food Engineering 78: 793-801.
Stojanovic, G., Radulovic, N., Hashimoto, T. and Palic, R., 2005. In vitro antimicrobial activity of extracts of four Achillea species: the composition of Achillea clavennae L. (Asteraceae) extract. Journal of Ethnopharmacology 101: 185-190.
Valant-Vetscheraa, K.M. and Wollenweberb, E., 1996. Comparative analysis of leaf exudate flavonoids in Achillea subsect. Filipendulinae. Biochemical Systematics and Ecology 24: 435-446.
Wang, J., Sun, B., Cao, Y., Tian, Y. and Li, X., 2008. Optimisation of ultrasound-assisted extraction of phenolic compounds from wheat bran. Food Chemistry 106: 804-810.