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2014 | 28 | 1 |

Tytuł artykułu

Modelling and analysis of compressive strength properties of parboiled paddy and milled rice

Treść / Zawartość

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
.

Słowa kluczowe

Wydawca

-

Rocznik

Tom

28

Numer

1

Opis fizyczny

p.73-83,fig.,ref.

Twórcy

  • Department of Agricultural Machinery, Ferdowsi University of Mashhad, P.O.Box. 91775-1163 Mashhad, Iran
  • Department of Agricultural Machinery, Ferdowsi University of Mashhad, P.O.Box. 91775-1163 Mashhad, Iran
autor
  • Department of Agricultural Machinery, Ferdowsi University of Mashhad, P.O.Box. 91775-1163 Mashhad, Iran
  • Department of Agricultural Machinery, Kurdistan University, Sanandaj, Iran

Bibliografia

  • Altuntas E. and Mehmet Y., 2007. Effect of moisture content on some physical and mechanical properties of faba bean (Vicia faba L.) grains. J. Food Eng., 78(1), 174-183.
  • Cao W., Nishiyama Y., and Koide S., 2004. Physicochemical, mechanical and thermal properties of brown rice grain with various moisture contents. Int. J. Food Sci. Technol., 39(9), 899-906.
  • Chen K.J. and Huang M., 2010. Prediction of milled rice grades using Fourier transform near-infrared spectroscopy and artificial neural networks. J. Cereal Sci., 52(2), 221-226.
  • Corręa P.C., da Silva F.S., Jaren C., Afonso Júnior P.C., and Arana I., 2007. Physical and mechanical properties in rice processing. J. Food Eng., 79, 137-142.
  • Ekinci K., Yilmaz D., and Ertekin C., 2010. Effects of moisture content and compression positions on mechanical properties of carob pod (Ceratonia siliqua L.). Afr. J. Agric. Res., 5(10), 1015-1021.
  • Fathi M., Mohebbi M., and Razavi S.M.A., 2011. Effect of osmotic dehydration and air drying on physicochemical properties of dried kiwifruit and modeling of dehydration process using neural network and genetic algorithm. Food Bioprocess Technol., 4, 1519-1526.
  • Goni S.M., Oddone S., Segura J.A., Mascheroni R.H., and Salvadori V.O., 2008. Prediction of foods freezing and thawing times: Artificial neural networks and genetic algorithm approach. J. Food Eng., 84, 164-178.
  • Gulati T.,Chakrabarti M., Singh A.,Duvuuri M., and Banerjee R., 2010. Comparative study of response surface methodology, artificial neural network and genetic algorithms for optimizationof soybean hydration. Food Technol. Biotechnol., 48(1), 11-18.
  • Izadifar M. and Jahromi M.Z., 2007. Application of genetic algorithm for optimization of vegetable oil hydrogenation process. J. Food Eng., 78(1), 1-8.
  • Kumar G.V.P., Srivastava B., and Nagesh D.S., 2009. Modeling and optimization of parameters of flow rate of paddy rice grains through the horizontal rotating cylindrical drum of drum seeder. Comput. Electron. Agr., 65(1), 26-35.
  • Parnsakhorn S. and Noomhorm A., 2008. Changes in physicochemical properties of parboiled brown rice during heat treatment. Agric. Eng. Int.: the CIGR E-journal. Manuscript FP 08 009. Vol. X.
  • Razavi S.M.A. and Farahmandfar R., 2008. Effect of hulling and milling on the physical properties of rice grains. Int. Agrophysics, 22, 353-359.
  • Sablani S.S. and Rahman M.S., 2003. Using neural networks to predict thermal conductivity of food as a function of moisture content, temperature and apparent porosity. Food Res. Int., 36(6), 617-623.
  • Saif S.M.H., Suter A.D., and Lan Y., 2004. Effects of processing conditions and environmental exposure on the tensile properties of parboiled rice. Biosystems Eng., 89(3), 321-330.
  • Salehi H., Zeinali Heris S., Koolivand Salooki M.K., and Noei S.H., 2011. Designing a neural network for closed thermosyphon with nanofluid using a genetic algorithm. Braz. J. Chem. Eng., 28, 157-168.
  • Shitanda D., Nishiyama Y., and Koide S., 2001. Performance analysis of an impeller husker considering the physical and mechanical properties of paddy rice. J. Agric. Eng. Res., 79(2), 195-203.
  • Shitanda D., Nishiyama Y., and Koide S., 2002. Compressive strength properties of rough rice considering variation of contact area. J. Food Eng., 53(1), 53-58.
  • Shopova E.G. and Vaklieva-Bancheva N.G., 2006. BASIC – A genetic algorithm for engineering problems solution. Comput. Chem. Eng., 30(8), 1293-1309.
  • Singh K.P., Mishra H.N., and Saha S., 2010. Moisturedependent properties of barnyard millet grain and kernel. J. Food Eng., 96(4), 598-606.
  • Wongrat W.,Younes A., Elkamel A., Douglas P.L., and Lohi A., 2011. Control vector optimization and genetic algorithms for mixed-integer dynamic optimization in the synthesis of rice drying processes. J. Franklin Inst., 348(7), 1318-1338.
  • Yang L., Peng L., Zhang L., Zhang L., and Yang S., 2009.Aprediction model for population occurrence of paddy stem borer (Scirpophaga incertulas), based on back propagation artificial neural network and principal components analysis. Comput. Electron. Agr., 68(2), 200-206.
  • Zareiforoush H.,Komarizadeh M.H., and Alizadeh M.R., 2010. Mechanical properties of paddy grains under quasi-static compressive loading. New York Sci. J., 3(7), 40-46.
  • Zhang Q., Yang X.S., Mittal G.S., and Yi S., 2002. Prediction of performance indices and optimal parameters of rough rice drying using neural networks. Biosystems Eng., 83(3), 281-290.
  • Zhang Q., Yang W., and Sun Z., 2005. Mechanical properties of sound and fissured rice kernels and their implications for rice breakage. J. Food Eng., 68(1), 65-72.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

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