Modeling of the some selected chemical properties of black Cumin seeds’ oil influenced by microwave pretreatment and screw rotational speed

مجله پژوهشهای علوم و صنایع غذایی ایران

Article 2, Volume 14, Issue 1 - Serial Number 49, March and April 2018, Pages 17-26   

Document Type: Research Article

DOI: 10.22067/ifstrj.v1395i0.57722

Authors

Hamid Bakhshabadi^{* 1}; Habibollah Mirzaee¹; Alireza Ghodsvali²; Seyed Mahdi Jafari¹; Aman Mohammad Ziaiifar¹

¹Department of Food Materials and Processing Design Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Iran.

²Agricultural Engineering Research Department, Golestan Agricultural and Natural Resources Research and Education Center, AREEO, Gorgan, Iran.

Abstract

Introduction: Black Cumin seed (Nigella sativa L.) as one of the novel edible oil resources used commonly nowadays as seasoning in food product industries due to considerable medicinal properties and high nutritional impacts. Oil extraction by pressing method as an approach compared to other methods including solvent extraction is faster, safer and cheaper. In the oil extraction process, the preparation of the seeds is a substantial stage for obtaining oil with high quality and efficiency. Microwaves are electromagnetic waves that have a frequency ranged from 300 MHz to 300 GHz with corresponding wave lengths ranged from 1 mm to 1 m. On the other hand the artificial neural network as a powerful predictive tool in a wide scale of process parameters has been studied on an industrial scale in this research in order to achieve a simple, rapid, precise as well as effective model in the oil extraction of Nigella sativa L seed.

Materials and Methods: In the present study Black Cumin seeds after preparation including cleaning and passing resistance of the samples in front of air and moisture were stored in a plastic bag until the day of experiments. Then, they have been pre-treated with microwave within different processing times (90, 180 and 270 S) and powers (180, 540, and 900 W). Afterwards, seeds’ oil was extracted by screw rotational speed levels approach (11, 34 and 57 rpm). Different selected parameters including extraction efficiency, oil acidity value, color and oxidative stability were determined. To predict the alterations trend, the artificial neural network (ANN) design in MATLAB R2013a software was used.

Results and Discussion: According to MSE and R2 values obtained in this study, feed forward neural network with transfer function sigmoid hyperbolic tangent and Levenberg- Marquardt learning algorithm with topology of 3-10-5 (input layer with 3 neurons– a hidden layer with 10 neurons – output layer with 5 neurons) were selected as the optimal neural network with R2 more than 0.995 and MSE equal to 0.0005. Also, the results of the optimized and selected models were evaluated and these models with high correlation coefficients (over 0.949), 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. By determining the activation function in neural networks which was a function of sigmoid hyperbolic tangent in this study and also, with having the amounts of weight and bias, the connections created by the neuro-fuzzy model can be extracted. By defining this simple created mathematical equation, in computer software such as Excel, we can have a useful, simple and accurate program for predicting the desired parameters in the process of oil extraction by using microwave pre-treatment. Due to high accuracy of neural model we can trust the prediction of these models with high confidence, and this model can be used to optimize and control the process, which can lead to saving in energy and time, and on the other hand, can create a better final product.

Keywords

oil extraction; Black; Cumin seed; Screw rotational speed; Microwave; Modeling

References

امیرمرادی، ش. و رضوانی مقدم، پ.1390. اثر تراکم و زمان مصرف نیتروژن بر خصوصیات مورفولوژیکی، مراحل فنولوژیکی، عملکرد و اجزای عملکرد سیاه‌دانه. نشریه علوم باغبانی(علوم و صنایع کشاورزی). 25: 251- 260.

گلی،ا. ح.، کدیور، م.، بهرامی، ب. و سبزعلیان، م.ر. 1386. خصوصیات فیزیکی و شیمیایی روغن دانه ماریتیغال. فصلنامه علوم و صنایع غذایی ایران. 4: 241- 254.

Anderson, D. 1996. A primer on oils processing technology. In Y. H. Hui (Ed) Bailey's industrial oil and fat products. JohnWiley and Sons, Inc., New York. 4: 10-17.

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.

Antonio, J.Y. and Dorado, M.P. 2006. A neural network approach to simulate biodiesel production from waste olive oil. Energy Fuels. 20:399–402.

Atta, M.B. 2003. Some characteristics of nigella (Nigella sativa L.) seed cultivated in Egypt and its lipid profile. Journal of Food Chemistry. 83: 63-68.

Azadmard, D.S., Habibi, N. F., Hesari, J., Nemati, M. and Fathi, A. B. 2010. Effect of pretreatment with microwaves on oxidative stability and nutraceuticals content of oil from rapeseed. Food Chemistry. 121, 1211–1215.

Cheikh-Rouhou, S., Besbes, S., Hentati, B., Blecker, C., Deroanne, C. and Attia, H. 2007. N. sativa L.: Chemical composition and physicochemical characteristics of lipid fraction. Journal of Food Chemistry. 101(2), 673-681.

Karaman, S., Ozturk, I., Yalcin, H., Kayacier, A. and 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.

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

Kittiphoom, S. and 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.and 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.

Lou, Z., Wang, H., Zhang, M. and Wang, Z. 2010. Improved extraction of oil from chickpea under ultrasound in a dynamic system. Journal of Food Engineering. 98: 13-18.

Lu, B., Zhang, Y., Wu, X. and 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. 50–63.

Mandal, V., Mohan, Y. and Hemalatha, S. 2007. Microwave Assisted Extraction – An Innovative & Promising Extraction Tool for Medicinal Plant Research. Pharmacognosy Reviews. 1: 8-14.

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

Meireles, A. and Angela, M. 2003. Supercritical extraction from solid: Process design data. Current Opinion in Solid State and Materials Science.7: 321–330.

Przybylski, R. and 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.

Savoire, R., Lanoiselle, J.L. and Vorobiev, E. 2013. Mechanical continuous oil expression from oilseeds: a review. Food and Bioprocess Technology. 6 (1), 1–16.

Sultana, B., Anwar, F. and Przybylski, R. 2007. Antioxidant potential of corncob extracts for stabilization of cornoil subjected to microwave heating. Food Chemistry. 104: 997–1005.

Singer, A., Nogala-Kalucka, M. and Lampart-Szczap, E. 2008. The content and antioxidant activity of phenolic compounds in cold-pressed plants oil. Journal of Food Lipids.15: 137-149.

Singh, P., Kumar, R., Sabapathy, S.N. and Bawa. S. 2008. Functional and edible uses of soy protein products. Comprehensive Reviews in Food Science and Food Safety. 7(1), 14-28.

Taghvaei, M., Jafari, S.M., Assadpoor, E., Nowrouzieh, S. and 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. and 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.

Wang, L. and Weller, C.L. 2006. Recent advances in extraction of nutraceuticals from plants. Trends Food Science and Technology. 17: 300-12.

Yolmeh, M., Habibi Najafi, M.B., Salehi, F. 2014. GA-ANN and ANFIS modeling of antibacterial activity of annatto dye on Salmonella enteritidis. Microb. Pathogenesis. 67: 36-40.

Zeng, X., Han, Z. and Zi, Z. 2010. Effect of Pulse Electric Field Treatment on Quality of Peanut Oil. Food Control. 21: 611- 614