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Predicting apparent metabolizable energy of wheat and corn based on the nutrient components and essential amino acids in broilers , using artificial neural network | ||
| علوم دامی | ||
| Article 19, Volume 28, Issue 106, June 2015, Pages 209-218 PDF (781.02 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.22092/asj.2015.101361 | ||
| Authors | ||
| h. mirzaei* ; Mohammad Salehi Dindarloo | ||
| Abstract | ||
| Three Artificial Neural Networks (ANN) models; General Regression Neural Network (GRNN), Redial Basis Function (RBF) and Three Layer Multiple Perceptron Network were carried out to evaluate the prediction of the apparent metabolizable energy (AME) of wheat and corn from its chemical composition in broiler. Input variables included: gross energy (GE), crude protein (CP), crude fiber (CF), ether extract (EE), ash and phosphorous as well as essential amino acids profiles (Arg, Cys, His, Ile, Leu, Lys, Met, Met+Cys, Phe, Thr and Trp). Output variable was AME of wheat or corn feedstuffs. The results showed that R2 ofThree Layers Perceptron Neural Network is higher than other two models in both wheat and corn. The best estimation for wheat and corn resulted from the CP (R2=0/89) and GE (R2=0/97) inputs, respectively. In wheat, RBF model had better estimation than GRNN model in all inputs except for the amino acids input. The RBF model was poorly estimated only with gross energy input. In corn, GRNN model has lower estimation than two other networks except gross energy input. Thus it was concluded that the artificial neural networks can be a powerful tool for predicating metabolizable energy from its chemical composition than multiple linear regression in broilers | ||
| Keywords | ||
| Artificial Neural Networks; Apparent Metabolizable Energy; Wheat; Corn; Broiler | ||
| References | ||
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Ahmadi, H., Golian, A., Mottaghitalab, M. and Nariman-Zadeh, N. (2008). Prediction model for true metabolizable energy of feather meal and poultry offal meal using group method of data handling- type neural network. Poultry Science, Vol, 87, pp:1909–1912. AOAC. (1990). Official Methods of Analysis. 15th edn. Association of official analytical chemists. Arlington, U. S. A. Dale, N. (1994). Relationship between bushel weight, metabolizable energy and protein content of corn from an adverse growing season. The Journal of Applied Poultry Research, Vol, 3, pp:83–86. Lessire, M., Hallouis, J.M., Barrier-Guillot, B., Champion, M. and Femenias, N. (2003). Prediction of the metabolisable energy value of maize in adult cockerel. British. Poultry Science, Vol, 44, pp:813–814. Lesson, S., Caston, L. and Summers, J.D. (1996). Broiler response to energy or energy and protein dilution in the finisher diet. Poultry Science, Vol, 75, pp: 529-535. Menhaj, M. (2000). Principles of Artificial Neural Networks. Amir-Kabir University press. Mollah, Y. and Annison, E.F. (1981). Prediction of metabolizable energy of wheat, maize and sorghum in poultry diets from chemical compositon. Proc. Nutr. Soc. Aust, Vol, 6, P.137. Roush, W.B. and Cravener, T.L. (1997). Artificial Neural Network Prediction of Amino Acid Levels In Feed Ingredients. Poultry Science, Vol, 76, pp: 721–727. Roush, W.B. and Cravener, T.L. (1999). Metabolism and Nutrition Improving Neural Prediction Of Amino Acid Levels In Feed Ingredients. Poultry Science, Vol, 78, pp: 983-991. Sarani, F. (1390). Prediction of the ammont of essential amino acids in 2 cereal samples using Artificial Neural Network. Master's thesis of Zabol university. Sedghi, M., Ebadi, M.R., Golian, A. and Ahmadi, H. (2011). Estimation and modeling true metabolizable energy of sorghom grain for poultry. Poultry Science, Vol, 90, pp: 1138-1143. Shakibai, A. and Koochekzadeh, S. (2009). Modeling and Predicting Agricultural Energy Consumption in Irn. American- Eurasian J. Agric.&. Environ. Sci., Vol, 5(3), pp: 308-312. Sibbald, I.R. (1989). Metabolizable energy evaluation of poultry diets. In: Recent Development in Poultry Nutrition. Edit. Cole, D. J. A., W. Haresign Butterworths. London. Silva, E.P.D., Rabello, C.B., Albino, L.F.T., Ludke, J.V., Lima, M.B. and Junior, W.M.D. (2010). Prediction of metabolizable energy values in poultry offal meal for broiler Chickens. Sociedade Brasileira de Zootecnia, ISSN, 1806-9290. Soleimani Rodi, P., Golian, A. and Sedghi, M. (1391). Estimates of metabolizable energy and digestible amino acids of wheat by using mathematical models. Iranian Fifth Congress on Animal Science, University of Esfahan. P: 442-446. Zhao, F., Zhang, H.F., Hou, S.S. and Zhang, Z.Y. (2008). Predicting metabolizable energy of normal corn from its chemical composition in adult Pekin ducks. Poultry Science, Vol, 87, pp:1603–1608. | ||
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