مدل‌سازی فازی- عصبی و سطح پاسخ آبگیرى اسمزی دانه‌های انار

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشگاه علوم کشاورزی و منابع طبیعی گرگان

2 دانشگاه فردوسی مشهد

چکیده

در این پژوهش دانه‌های انار به روش اسمزی، با محلول‌های40، 50 و 60 درصد ساکارز در دماهای 45، 55 و 65 درجه سلسیوس فرایند شدند و مقدار جذب موادجامد، کاهش آب و کاهش وزن نمونه‌ها در زمان‌های 60، 120، 180 دقیقه اندازه‌گیری گردید. فرایند آبزدایی اسمزی با ترکیب تکنیک­های منطق فازی و شبکه‌های‌ عصبی‌مصنوعی (مدل‌سازی فازی- عصبی) و روش سطح پاسخ مدل‌سازی شد. برای مدل‌سازی، درون‌یابی و افزایش داده‌ها، از منطق فازی استفاده شد و با وارد کردن نتایج مدل فازی در شبکه‌های عصبی‌مصنوعی، شبکه پس‌انتشار پیشخور با توپولوژی 3-8-3، ضریب‌همبستگی 98344/0 و میانگین مربعات خطای 02278/0 با تابع فعال‌سازی لگاریتمی و الگوی یادگیری لونبرگ – مارکوات به عنوان بهترین مدل عصبی ارائه گردید. مدل‌های رگرسیونی ایجاد شده با استفاده از روش سطح پاسخ نیز با ضریب همبستگی بیش از 91/0 توانایی بالایی برای پیش‌بینی فاکتورهای پاسخ داشتند ولی در مقایسه با مدل‌ فازی- عصبی از دقت پایین‌تری برخوردار بودند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Neuro-fuzzy and response surface modeling of osmotic dehydration of pomegranate arils

نویسندگان [English]

  • Mohammad Ganjeh 1
  • Seyed Mahdi Jafari 1
  • Sajjad Ghaderi 2
چکیده [English]

In this research, pomegranate arils are dehydrated by osmotic dehydration in 40, 50, and 60 % sucrose ‎solutions and at 45, 55 and 65 degrees C‎‏ ‏‎ and Weight Reduction, Solids grain and Water Loss of the products ‎were measured at 60, 120 and 180 minutes of process. Osmotic dehydration processes was modeled by ‎combination of neural network and fuzzy logic techniques (Neuro-fuzzy) and response surface methodology. ‎For modeling, interpolation and increase of the data’s, fuzzy logic was used. By entering the obtained results ‎from fuzzy model into the neural network tool, the Feed-Forward-Back-Propagation network with the ‎topology of 3-8-3 and the correlation coefficient of 0.98344‎‏ ‏and mean square error of 0.02278‎‏ ‏with ‎application of Log-sigmoid transfer function‏ ‏‎(logsig) and Levenberg–Marquardt learning algorithm was ‎determined as the best neural model. Regression models created by response surface methodology by ‎correlation coefficient of 0.90 were also capable for prediction of response factors but in comparison with ‎Neuro-fuzzy models have a lower accuracy.‎

کلیدواژه‌ها [English]

  • modeling
  • neural network
  • fuzzy logic
  • Response surface
  • Pomegranate arils
Al-Maiman, S.A. & Ahmad, D. (2002).Changes in physical and chemical properties during pomegranate (Punica granatum L.) fruit maturation. Journal of  Food Chemistry  76, 437–41.

Alvarez Lopez, I., Lianes, S. & Verdegay, J. L. 2005. Drying process of tobacco leaves by using a fuzzy controller. J. Fuzzy Sets and Sys. 150, 493-506.

Arsdel, W.B.V & Copleg, M.J. (1963). Food Dehydration. Vol. 1. AVI. Publishing Co.

Atkinson, A.C. & Donev, A.N. (1992). Optimum experimental design. Oxford University Press 5,132-189.

Atthajariyakul, S. & Leephakpreeda, T. (2006). Fluidized bed paddy drying in optimal condition via adaptive fuzzy logic control. Journal of  Food Engineering. 75, 104-114.

Azoubel, P. & Murr, F. (2004). Mass transfer kinetics of osmotic dehydration of cherry tomato. Journal of  Food Engineering. 61. 291–95.

Bardot, I., Martin, N., Trystram, G., Hossenlopp, J., Rogeaux, M. & Bochereau, L. (1994). A new approach for the formulation of beverages. Part II: interactive automatic method. Lebensmittel-Wissenschaft und-Technology. 27 (6), 513–521.

Baruch, I., Genina-Soto, P., Nenkova, B. & Barrera-Cortes, J. (2004). Neural model of osmotic dehydration kinetics of fruits cubes. Lecture Notes in Artificial Intelligence. Subseries of Lecture Notes in Computer Science, 3192, 312–320.

Bchir, B., Besbes, S., Attia, H. & Blecker, C. (2009). Osmotic dehydration of pomegranate seeds: mass transfer kinetics and differential scanning calorimetry characterization,  International Journal of Food Science and Technology. 44, 2208–221.

Beristian, C.I., Azuara, E., Cortes, R. & Garcia, H.S. (1990). Mass transfer during osmotic dehydration of pineapple rings.  International Journal of  Food Science Technology. 25 (5), 576–582.

Bla Zita, N. E., Emmanuel, N., Patrice, K., Ismael, D. & Benjamin, Y. (2009). Modeling of Osmotic Dehydration of Mango (Mangifera Indica) by Recurrent ArtificialNeural Network and Experimental Design. Research Journal of Agriculture and Biological. Science. 5(5), 754-761.

Bolin, H.R. (1983). Effects of osmotic agents and concentration on fruit quality. Journal of Food Science. 48, 202-205.

Chen, C. R., Ramaswamy, H. S. & Alli, I. (2001). Prediction of quality changes during osmo-convective drying of blueberries using neural network models for process optimization. Drying Technology. 19(3&4), 507–523.

Dioxon. G.M. & J.J. JEN. (1977). Changes of sugars and acids of osmovac dried apple slices. Journal of Food Science. 42, 1126-1127.

Falade, KO., Igbeka, J. & Funke, A. 2007. Kineticmass transfer and colour changes during somtoic dehydration of water melon. Journal of Food Engeeniring. 80, 979–85.

Fazel zarandi, M. H. (2002). Fuzzy complexes theory (rendition). Amir kabir university publication (poly technique). (In Farsi)

Ganjeh, M., Jafari, S.M., Ghanbari, V., Dezyani, M., Ezzati, R., Soleimani, M. (2013). Modeling the drying kinetics of onion in a fluidized bed drier equipped with a moisture controller using regression, fuzzy logic and artificial neural networks methods. Iranian Journal of  Nutrition Sciences & Food  Technology.7 (5). 399-407. (In Farsi)

Ghoush, M.A., Samhouri, M., Al-Holy, M. & Herald, T. 2008. Formulation and fuzzy modeling of emulsion stability and viscosity of a gum-protein emulsifier in a model mayonnaise system. Journal of Food Engineering. 84, 348–357.

Glaucia, S., Leila, M. & Miriam, D. (2012).  Optimization of osmotic dehydration process of guavas by response surface methodology and desirability function, International Journal of  Food Science Technology. 47, 132–140.

Hawkes, J. & Flink, J.M. (1978). Osmotic dehydration of fruit slices prior to freeze dehydration. Journal of Food Processing. 2 (4), 265–284.             

Holdsworth, S.D. (1986). Advance in dehydration of fruits and vegetables. In “Concentration and Drying of food”. D. McCarthy, Editor. Elsevier Applied Sci. Pub. LTD.

Jindal, V.K. & hauhan, V. (2001).  Neural networks approach to modeling food processing operations.  In:  Irudayaraj, J. (Ed.), Food Processing Operations Modeling: Design and Analysis. Marcel Dekker, New York, pp. 305–342.

Jumah, R. & Mujumdar, A. S. (2005). Model intermittent drying using adaptive neuro-fuzzy inference system. Drying Technology, 23(5), 1075–1092.

Karel, M. (1975). Dehydration of foods. In “Principles of Food Science. Part 2. Physical principles of food preservation”. O.R. Fennem, Editor. Mareel Dekker Pub.

Kargozari, M., Moini, S. & Emam Djomeh, Z. (2010) Prediction of some physical properties of osmodehydratied carrot cubes using response surface methodology. Journal of food processing and preservation. 34: 1041-1063.

Kargozari, M.,  Moini, S.,  Emam-djomeh, Z. &  Khodayian, F. (2007). Optimization of osmotic dehydration carrot using response surface methodology. The proceeding of the 5TH asla-pacific drying conference. Pp:1150-1156.

Kia, S. M. (2010). Fuzzy logic in matlab. First Edition .Kian Rayane Sabz Pub.( In Farsi)

Kingsly, A. R., Singh, B. D., Manikantan, M.R. and Jain, R. K. 2006. Moisture dependant physical properties of dried pomegranate seeds (Anardana). Journal of  Food Engeeniring 75:492–96.

Lazarides, H. 1999. Advance in osmotic dehydration in processing food, (eds, F.A.R.olivera at al) CRC Press. Newyork: (pp.179-196).

Lazarides., H. & kastanidis, E. (1994). Mass teransfer kinetics during osmotic preconcentration aiming at minimal solid uptake. Journal of  Food Engeeniring 24(4), 110-119.

Lenart, A. (1989.). Osmotic dehydration of apples at high temperature in drying. Hemisphere Pub.

Lenart, A. & lewicki, P.P.(1988). Energy consumption during osmotic and convective drying of plant tissue. Acta Alimentaria Polonica.1:65-72.

Lerici, C.R., Pinnavaia, G., Dalla Rosa, M. & Bartolucci, L. (1985). Osmotic dehydration of fruit: influence of osmotic agents on drying behaviour and product quality. Journal of  Food science. 50 (5), 1217–1219.

Lerici, C. (1989). Osmotic dehydration. Journal of  Food science. 5, 1214-1219.

Lertworasirikul, S. & Saetan, S. (2010). Artificial neural network modeling of mass transfer during osmotic dehydration of kaffir lime peel. Journal of  Food Engeeniring. 98 , 214–223.               

Linko, P. & Zhu, Y.H. (1991). Neural network programming in bioprocess variable estimation and state prediction. Journal of  bio thecnalogy. 21, 253–270.

Magee, T.R.A., Hassaballah, A.A. & Murphy, W.R. (1983). Internal mass transfer during osmotic dehydration of apple slices in sugar solution.  Irish Journal of  Food science Technology. 7, 147–155.

Manivannan, P. & Rajasimman, M. (2008). Osmotic dehydration of beetroot in salt solution: optimization of parameters through ststistical experimental design. Internatiol journal of chemistry and Biological Engeeniring. 1:4.

Mudahar, S., Toledo, T. & Jen, J. (2007). A response surface methodology approach to optimize potato dehydration process. Journal of  Food Processing and Preservation.  14(2),  93–106.

Mundada, M., Singh Hathan, B. & Maske S. (2011). Mass Transfer Kinetics during Osmotic Dehydration of Pomegranate Arils. Journal of Food Science, 76(1).

Nazni, P. and Thara, D. (2011). Optimization of beetroot peel osmotic dehydration process using response surface methodology. Internatiol journal of  Current Research, 3(8), 27-32.

Ochoa-Martínez, C.I. & Ayala-Aponte, A.A. (2007). Prediction of mass transfer kinetics during osmotic dehydration of apples using neural networks. Lebensmittel-Wissenschaft und-Technology. 40 (4), 638–645.

 Ochoa-Martínez, C. I.  Ramaswamy, H. S. &  Ayala-Aponte. A. A. (2007). Artificial Neural etwork Modeling of Osmotic Dehydration Mass Transfer Kinetics of Fruits. Drying Technology: An International Journal, 25(1),85-89.

Odetunji, O. A. & Kehinde, O. O. (2005). Computer simulation of fuzzy control systemfor gari fermentation plant. Journal of Food Engineering. 68, 197–207.

Poligne, I., Broyart, B., Trystram, G. & Collignal, A. (2002). Prediction of mass transfer kinetics and product quality changes during a dehydration–impregnation–soaking process using artificial neural net-works. Application to pork curing. Lebensmittel-Wissenschaft und-Technology. 35, 748–756.

Raoult-Wack,  A.L. (1994).  Recent advances in the osmotic dehydration of foods.Trends in Food Science Technology. 5 (8), 255–260.

Rastogi, NK. & Raghavarao, K. (2004). Mass transfer during osmotic dehydration of pineapple: considering Fickian diffusion in cubical configuration. LWT- Food Science Technology. 37, 43–7.

Robles, M.F.C., Casado, O., Syafiie, S. & Tadeo, F. 2006. Fuzzy control of a neutralization process. Engineering Applications of Artificial Intelligence. 19,905–914.

Salvatori, D., Andr´es, A., Chiralt, A. & Fito, P. (1999). Osmotic dehydration progression in apple tissue I: spatial distribution of solutes and moisture content. Journal of Food Engeeniring. 42 (3), 125–132.

Shi, J. and  Le Maguer, M. (2002). Osmotic dehydration of foods: mass transfer modeling aspects. Food Review International 18(4), 305–35.

Tortoe, C. Orchard, J. Beezer, A.  & Tetteh, J. (2008) artificial neural networks in modeling osmotic dehydration of foods. Journal of Food Processing and Preservation. 32, 270–285.

Toupin, C.J., Marcotte, M., Le Maguer, M. (1989). Osmotically induced mass transfer in plant storage tissues, part I:  a mathematical model.  Journal of Food Engeeniring. 10 (1), 13–38.

Trelea, I.C., Raoult-Wack, A.L. and Trystram, G. (1997).  Note:  application  of  neural network  modeling  for  the  control  of  dewatering  and  impregnation  soaking process (osmotic dehydration). Food Science. Technology International. 3(6), 459–465.

Vaquiro, H. A., Bon J. and Dies, J. L. (2008). Fuzzy logic application to drying kinetics modeling. In: Proceedings of the 17th World Congress the International Federation of Automatic Control Seoul, Korea: 2206 - 2211.

Vardin, H., Fenerciog˘lu H. (2003). Study on the development of pomegranate juice processing technology: clarification of pomegranate juice. Nahrung 47:300–03.