مدل‌سازی استخراج روغن از دان سیاه به کمک پیش تیمار ترکیبی ریزموج – میدان الکتریکی پالسی با شبکه‌های عصبی مصنوعی

نویسندگان
نازنین مریم محسنی 1
حبیب الله میرزایی 2
معصومه مقیمی 3
1 گروه علوم و صنایع غذایی، واحد ساری، دانشگاه آزاد اسلامی، ساری، ایران
2 دانشیار، دانشکده علوم و صنایع غذایی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، ایران.
3 گروه شیمی، واحد گنبد کواوس، دانشگاه آزاد اسلامی، گنبد کاووس، ایران
چکیده
در این تحقیق به منظور مدل‌سازی فرایند استخراج روغن از دان سیاه با استفاده از پیش‌تیمار ترکیبی ریزموج- میدان الکتریکی پالسی از 3 سطح زمان ریزموج (0، 100 و 200 ثانیه) و سه سطح شدت میدان الکتریکی (0، 5/2 و 5 kV/cm) استفاده گردید و بعد از اعمال این پیش‌تیمارها، روغن دانه‌ها با پرس مارپیچی و با سرعت‌های متفاوت (11 تا 57 دور در دقیقه) استخراج گردید و میزان کارایی فرایند استخراج روغن، دانسیته، رنگ، پایداری اکسیداتیو، ترکیبات فنولی و پروتئین کنجاله مورد بررسی قرار گرفت. جهت پیش‌بینی روند تغییرات از ابزارشبکه‌های عصبی مصنوعی استفاده شد. با بررسی شبکه‌های مختلف شبکه‌ی پس‌انتشار پیشخور با توپولوژی‌های 3-9-6 با ضریب همبستگی بیشتر از 995/0 و میانگین مربعات خطای کمتر از 001/0 و با بکارگیری تابع فعال‌سازی لگاریتم سیگموئیدی، الگوی یادگیری جهنده و چرخه یادگیری 1000 به عنوان بهترین مدل‌ عصبی مشخص گردید. از طرفی نتایج نشان داد که افزایش زمان ریزموج و همچنین شدت میدان الکتریکی در ابتدا منجر به افزایش میزان کارایی فرایند استخراج روغن گردید ولی با افزایش بیشتر این دو پارامتر میزان کارایی فرایند استخراج روغن کاهش یافت. میزان اسیدیته روغن‌ها نیز با افزایش زمان ریزموج، شدت میدان الکتریکی و سرعت دورانی پرس مارپیچی افزایش یافت. یافته‌های حاصل از مدل‌های بهینه‌ی انتخاب شده نیز ارزیابی گردید و این مدل‌ها با ضرایب همبستگی بالا )بیش از 918/0( قادر به پیش‌بینی روند تغییرات نمونه‌های روغن تولیدی با پیش تیمار ریزموج- میدان الکتریکی پالسی بودند.
کلیدواژه‌ها
استخراج روغن
دان سیاه
پیش تیمار ترکیبی مایکروویو- میدان الکتریکی پالسی
شبکه‌های عصبی مصنوعی

موضوعات

عنوان مقاله English

Modeling the oil extraction from Niger seeds using the combinational pretreatment of Microwave- pulsed electric field with artificial neural networks

نویسندگان English

nazanin maryam mohseni 1
Habib Ollah mirzaei 2
Masoumeh Moghimi 3
1 Department of Food Science and Technology, Sari Branch, Islamic Azad University, Sari, IranNazanin.mohsenii@gmail.com
2 Associate Professor, Department of Food Science and Technology, University of Agricultural Sciences and Natural Resources, Gorgan, Iran.
3 Department of Chemistry, Gonbad Kavoos Branch, Islamic Azad University, Gonbad Kavoos, Iran
چکیده English

In this research to model the process of extracting oil from Niger seeds using the combinational pretreatment of microwave-pulsed electric field three microwave time levels of (0, 100, and 200 seconds) and three electric field intensity levels of (0, 2.5 and 5 kV/cm) were used and after applying these pretreatments, the oil of seeds were extracted using the screw press with different speeds (11 to 57 rpm) and the efficiency of oil extraction process, density, color, oxidative stability, phenolic compounds and protein amount of meal were considered. The artificial neural network tool was used to predict the variations process. Through studying and examining various networks, the feed forward back propagation network with 6-9-3 topologies and with correlation coefficient of more than 0.995 and mean squared error less than 0.001 using logarithm sigmoid activation function, resilient learning pattern and learning process of 1000 were determined as the best neural method. On the other hand the results indicated that an increase in the microwave time and also in the electric field intensity at first led to increase in the efficiency of oil extraction process but with more increase in these two parameters the efficiency amount of oil extraction process was decreased. Also with increase in the microwave time, electric field intensity and the rotational speed of screw press the acidity amount of oils was increased too. The results obtained from selected optimized models were evaluated too and these models with high correlation coefficient (over 0.918) were able to predict the variation process of oil samples produced using microwave-pulsed electric field pretreatment.

کلیدواژه‌ها English

oil extraction
Niger seed
combinational pretreatment of microwave – pulsed electric field
artificial neural network
1. Riley, K.W. and Belayneh, H. 1989. Niger. Oil Crops of the World. 394-403.
2. Seegeler, C.J.P. 1983. Oil plants in Ethiopia. Their taxonomy and agricultural significance. Centre for Agricultural Publication and Documentation, PUDOC, Wageningen.
3. Rostami, M., Farzaneh, V. Boujmehrani, A., Mohammadi, M. and Bakhshabadi, H. 2014. Optimizing the extraction process of sesame seeds oil using response surface method on the industrial scale. Industrial Crops and Products. 58, 160–165.
4. Bakhshabadi, H., Mirzaei, H.O., Ghodsvali, A., Jafari, S.M., Ziaiifar, A.M. and Farzaneh. V. 2017. The effect of microwave pretreatment on some physic-chemicalproperties and bioactivity of Black cumin seeds’ oil. Industrial Crops and Products. 97: 1–9.
5. 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.
6. Cravotto, G., Boffa, L., Mantegna, S., Perego, P., Avogadro, M. and Cintas, P. 2007. Improved extraction of vegetable oils under high-intensity ultrasound and/or microwaves. Ultrasonics Sonochemistry. 15(5): 898-902.
7. Jiao, J., Li, Z.G., Gai, Q-Y, Li, X.G., Wei, F.U., Fu, Y.J. and Ma, W. 2014. Microwave-assisted aqueous enzymatic extraction of oil from pumpkin seeds and evaluation of its physicochemical properties, fatty acid compositions and antioxidant activities. Food Chemistry. 147: 17-24.
8. Momeny, E., Rahmati, S. and Ramli, N. 2012. Effect of Microwave Pretreatment on the Oil Yield of Mango Seeds for the Synthesis of a Cocoa Butter Substitute. Journal of Food Processing & Technology. 3(7): 1-7.
9. Qu, X.J., Fu, Y.J., Luo, M., Zhao, C.J., Zu, Y.G., Li, C.Y., Wang, W., Li, J. and Wei, Z.F. 2013. Acidic pH based microwave-assisted aqueous extraction of seed oil from yellow horn (Xanthoceras sorbifolia Bunge.). Industrial Crops and Products. 43: 420– 426.
10. Quass, D. W. 1997. Pulsed electric field processing in the food industry. A status report on pulsed electric field. Palo Alto, CA. Electric Power Research Institute. CR- 109742. Pp:23-35.
11. Barbosa-Canovas, G. V. and Zhang, Q. H. 2001. In Pulsed Electric Fields in Food Processing Fundamentals Aspects and Applications. Lancaster, PA: Technomic Publishing Co., Inc.
12. Asavasanti, S., Ristenpart, W., Stroeve, P. and Barrett, D.M. 2011. Permeabilization of Plant Tissue by Monopolar Pulsed Electric Field: Effect of Frequency. Journal of Food Science. 76(1): 98-111.
13. Salehi, M.A. and Omidvari, A.. 2015. Applying PEF in optimizing the energy use in extracting sugar from sugar beet. Scientific research journal of engineering and energy management. 5 (4): 26-33.
14. Guderjan, M., Elez-Martínez, P. and Knorr, D. 2007. Application of pulsed electric fields at oil yield and content of functional food ingredients at the production of rapeseed oil. Innovative Food Science and Emerging Technologies. 8: 55–62.
15. Guderjan, M., Topfl, S., Angersbach, A. and Knorr, D. 2005. Impact of pulsed electric field treatment on the recovery and quality of plant oils. Journal of Food Engineering. 67 (3): 281–287.
16. Knorr, D., Geulen, M., Grahl, T. and Sitzmann W. 1994. Food application of high electric field pulses. Trends in Food Science and Technology. 5: 71-75.
17. Sablani, S. S., Shafiur Rahman, M., Datta, A. K. and Mujumdar, A. S. 2007. Handbook of food and bioprocess modeling technology. CRC press. Pp: 378-380.
18. Wu, C. H. and McLarty, J.W. 2000. Neural Networks and Genome Informatics. Elsevier Publishing Co. USA. 220 p.
19. Farzaneh, V., Bakhshabadi, H., Gharekhani, M., Ganje, M., Farzaneh, F. and Carvalho, I.S. 2017 Application of an adaptive neuro_fuzzy inference system (ANFIS) in the modeling of rapeseeds’ oil extraction. Food process enginering. 125: 1-8.
20. Thakker, M.R., Parikh, J.K. and Desai, M.K. 2016. Microwave assisted extraction of essential oil from the leaves of Palmarosa: Multi-response optimization and predictive modelling. Industrial Crops and Products. 86: 311-319.
21. Vasseghian, Y., Zahedi, G,H. and 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.
22. Okunola, A. A. and Adepoju T. F. 2015. Modelling and optimization of extraction of oil from Sesam indicum seeds: A case study of response surface methodology vs. Artificial neural network. International Journal of Chemistry and Materials Research. 3(2): 41-52.
23. Uquiche, E., Jeréz, M. and Ort, Z.J. 2008. Effect of pretreatment with microwaves on mechanical extractionnyield and quality of vegetable oil from Chilean hazelnuts (Gevuina avellana Mol). Innovative Food Science and Emerging Technologies. 9: 495–500.
24. 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.
25. Bakhshabadi, H., Vahdani, M., Moghimi, M., Bazrafshan, M., Rashidzadeh, Sh. and Bojmehrani, A. 2017.Modeling the cooking process during the extraction of oil from Sunflower seeds using artificial neural networks on an industrial scale. Journal of Iranian Food Science and Technology. 69(14):1-11.
26. AOCS. 1993. Official Methods and Recommended Practices of the American Oil Chemists’ Society, AOCS Press, Champaign, IL. 762p.
27. Bail, S., Stuebiger, G., Krist, S., Unterweger, H. and Buchbauer, G. 2008. Characterisation of various grape seed oils by volatile compounds, triacylglycerol composition, total phenols and antioxidant capacity. Food Chemistry. 108, 1122–1132.
28. AOAC. 2008. Official methods of analysis of the association of official analytical chemists, Vol. II. Arlington, VA: Association of Official Analytical Chemists.
29. Dolatabadi, Z., Elhami Rad, A.H., Farzaneh, V., Akhlaghi Feizabad, S.H., Estiri, S.H and Bakhshabadi, H. 2016. Modeling of the lycopene extraction from tomato pulps. Food Chemistry. 190: 968-973.
30. Akintunde, A.M., Ajala, S.O. and Betiku, E. 2015. Optimization of Bauhinia monandra seed oil extraction via artificial neural network and response surface methodology: A potential biofuel candidate. Industrial Crops and Products. 67: 387-394.
31. 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.
32. Bakhshabadi, H., Vahdani, M., Moghimi, M., Bazrafshan, M., Rashidzadeh, Sh. and Bojmehrani, A. 2017. Modeling the cooking process during the extraction of oil from Sunflower seeds using artificial neural networks on an industrial scale. Journal Food Science and Technology .69(14): 1-11.
33. Mehrazin, A. R., Zendehdel, A. and Taghipour, M. 2013. Training Radial neural networks on the basis of statistical models variables and comparing them in predicting the bankruptcy, Investment knowledge scientific research quarterly. 2 (7): 149-166.
34. Bakhshabadi, H., Mirzaei, H.O., Ghodsvali, A., Jafari, S.M. and Ziaiifar, A.M. 2018. The influence of pulsed electric field and microwave pretreatments on some of the physicochemical properties of oil and the meal of black cumin seed . Food science and nutrition. 1–8.
35. Schroeder, S., Buckow, R. and Knoerzer, K. 2009. Numerical Simulation of Pulsed Electric Field (PEF) Processing for Chamber Design and Optimization, International Conference on CFD in the Minerads and Process Industries CSIRO, 17th, Australia
36. Sriti, J., Msaada, K., Talou, T., Faye, M., Kartika, I.A. and Marzouk, B. 2012. Extraction of coriander oil by twin-screw extruder: Screw configuration and operating conditions effect. Industrial Crops and Products. 40: 355– 360.
مجله علوم و صنایع غذایی ایران
دوره 16، شماره 86
فروردین 1398
صفحه 95-107