Modeling of oil Extraction From Flaxseed by Using Microwave Pretreatment With Artificial Neural Network

Moghimi, Masoumeh; Bakhshabadi, Hamid; Bazrafshan, Masoud

doi:10.22101/JRIFST.2017.09.02.627

Modeling of oil Extraction From Flaxseed by Using Microwave Pretreatment With Artificial Neural Network

Document Type : Original Paper

Authors

¹ Department of Chemistry, Gonbad Kavoos Branch, Islamic Azad University, Gonbad Kavoos, Iran

² Department of Food Science, Gonbad Kavoos Branch, Islamic Azad University, Gonbad Kavoos, Iran

10.22101/JRIFST.2017.09.02.627

Abstract

In the oil extraction, the suitable treatment of the seeds before extraction is the most critical steps to produce high quality and efficiency products. In this research, in order to model the process of oil extraction from flax seeds, the researchers applied pre-treated with microwave within different processing times (90, 180 and 270 S) and powers (180, 540, and 900 W) and the efficiency of oil extraction, acidity, refractive index, density, acid number, and the oil color were studied. To predict the changes' trend the artificial neural network in MATLAB R2013a software was used. The results showed that by increasing microwave time and power efficiency of oil extraction, index acid and acidity, density and oil color increased. Analysis of variance results showed that the studied microwave pre-treated had no effect on the refractive index. By studying the various networks of back propagation feed forward network with topologies 2-8-6 with a correlation coefficient of more than 0.999 and the mean squared error of less than 0.001 and with using sigmoid hyperbolic of tangent activation function, the Resilient back propagation and learning cycle of 1000 were specified as the best neural model. The results of the optimized and selected models were evaluated and these models with high correlation coefficients (over 0.844), were able to predict the changes' trend. According to the complexity and multiplicity of the effective factors in food industry processes and the results of this research, the neural network can be introduced as an acceptable model for modeling these processes.

Keywords

References

Antonio, J.Y., & Dorado, M.P. 2006. A neural network approach to simulate biodiesel production from waste olive oil. Energy Fuels, 20(1):399-402.

AOAC. 2008. Official methods of analysis of the association of official analytical chemists, Vol. II. Arlington, VA: Association of Official Analytical Chemists.

AOCS. 1993. Official methods and recommended practices of the American oil chemists society. AOCS Press, Champaign, IL. 762p.

Bakhshabadi, H., Mirzaei, H.O., Ghodsvali, A., Jafari, S.M., Ziaiifar, A.M., & Farzaneh. V. 2017. The effect of microwave pretreatment on some physico-chemicalproperties and bioactivity of Black cumin seeds’ oil. Industrial Crops and Products, 97: 1-9.

Dolatabadi, Z., Elhami Rad, A.H., Farzaneh, V., Akhlaghi Feizabad, S.H., Estiri, S.H., & Bakhshabadi, H. 2016. Modeling of the lycopene extraction from tomato pulps. Food Chemistry, 190:968-973.

Dulog, L. 1990. Leinoel und daraus abzuleitende Stoffe fuer die Beschichtung von Fussbodenbelag. Bundes Exp. Wissenschaftliches Zentrum. Bonn.

Food and Agriculture Organization (FAO) STAT. 2013. Food and agriculture organization of the united nation. http://faostat3.fao.org/ browse/ Q/ QC/ E (Accessedon 25Feb2015).

Goyal, N., Sharma, V., Upadhyay, N., Gill, S., & Sihag, M. 2014. Flax and flaxseed oil: an ancient medicine & modern functional food. Journal of Food Science and Technology, 51(9):1633-1653.

Iran-Nejad, H., & Hoseini Mazinani, S.M. 2005. The effect of planting date on the seed yield of three varieties of oil flax in Varamin. Journal of Agricultural Science, 11(4):111-120. [In Persian]

Izadifar, M., & Abdolahi, F. 2006. Comparison between neural network and mathematical modeling of supercritical CO2 extraction of black pepper essential oil. The Journal of Supercritical Fluids, 38(1): 37-43.

Karaman, S., Ozturk, I., Yalcin, H., Kayacier, A., & Sagdi, O. 2012. Comparison of adaptive neuro fuzzy inference system and artificial neural networks for estimation of oxidation parameters of sunflower oil added with some natural byproduct extracts. Journal of the Science of Food and Agriculture, 92(1): 49-58.

Khajehpour, M. 1991. Production of industrial plants. Isfahan Industrial University Jihad Publication, 251 p. [In Persian]

Khazaei, J., & Daneshmandi, S. 2007. Modeling of thin-layer drying kinetics of sesame seeds: mathematical and neural networks modeling. International Agrophysics, 21(4): 335-348.

Kittiphoom, S., & Sutasinee, S. 2015. Effect of microwaves pretreatments on extraction yield and quality of mango seed kernel oil. International Food Research Journal, 22(3):960-964.

Klaypradit, W., Kerdpiboon, S., & Singh, R.K. 2011. Application of artificial neural networks to predict the oxidation of menhaden fish oil obtained from Fourier transform infrared spectroscopy method. Food bioprocess Technology, 4(3):475-80.

Lu, B., Zhang, Y., Wu, X., & Shi, J. 2007. Separation and determination of diversiform phytosterols in food materials using supercritical carbon dioxide extraction and ultraperformance liquid chromatography–atmospheric pressure chemical ionization–mass spectrometry. Analytica Chimica Acta, 588(1): 50-63.

Machavaram, R., Jena, P.C., & Raheman, H. 2008. Predictionof optimized pretreatment process parametersfor biodiesel pro-duction using ANN and GA. Fuel journal, 88(5):868-875.

Maherani, B. 2001. Study of the extraction conditions and physicochemical properties of flaxseed gum. Master's thesis of food industry, Faculty of Agriculture, Tarbiat Modares University, 112 p. [In Persian]

Oomah, B.D. 2001. Flaxseed as functional source. Journal of the Science of Food and Agriculture, 81(9): 889-894.

Oomah, B.D., & Mazza, G. 1995. Fractionation of flaxseed with a batch dehuller. Industrial Crops and Products in International Journal, 9(1): 19-27.

Przybylski, R., & Zambiazi, R.C. 2000. Predicting oxidative stability of vegetable oils using neural network system and endogenous oil components. Journal of the American Oil Chemists' Society, 77(9): 925-932.

Rafiei Nazari, R., Arabameri, M., & Nouri, L. 2015. modeling and predicting the oxidative stability of olive oil during the storage time at ambient conditions using artificial neural network. Iranian Journal of Nutrition Sciences & Food Technology, 10(1):71-80. [In Persian]

Sanchez, R.J., Mateo, C.M., Fernández, M.B., & Nolasco, S.M. 2017.Bidimensional modeling applied to oil extraction kinetics of microwave-pretreated canola seeds. Journal of Food Engineering, 192: 28-35.

Sultana, B., Anwar, F., & Przybylski, R. 2007. Antioxidant potential of corncob extracts for stabilization of cornoil subjected to microwave heating. Food Chemistry, 104(3):997-1005.

Taghvaei, M., Jafari, S.M., Assadpoor, E., Nowrouzieh, S., & Alishah, O. 2014. Optimization of microwave-assisted extraction of cottonseed oil and evaluation of its oxidative stability and physicochemical properties. Food Chemistry, 160: 90-97.

Terigar, B.G., Balasubramanian, S., Sabliov, C.M., Lima, M., & Boldor, D. 2011. Soybean and rice bran oil extraction in a continuous microwave system: From laboratory- to pilot-scale. Journal of Food Engineering, 104(2): 208-217.

Vasseghian, Y., Zahedi, G.H., & Ahmadi, M. 2016. Oil Extraction from Pistacia Khinjuk - Experimental and Prediction by Computational Intelligence Models. Journal of Food Biosciences and Technology, 6(1):1-12.