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Prediction of the energy values of feedstuffs for broilers using meta-analysis and neural networks

  • F. C. M. Q. Mariano (a1), C. A. Paixão (a2), R. R. Lima (a1), R. R. Alvarenga (a3), P. B. Rodrigues (a3) and G. A. J. Nascimento (a4)...
Abstract

Several researchers have developed prediction equations to estimate the metabolisable energy (ME) of energetic and protein concentrate feedstuffs used in diets for broilers. The ME is estimated by considering CP, ether extract, ash and fibre contents. However, the results obtained using traditional regression analysis methods have been inconsistent and new techniques can be used to obtain better estimate of the feedstuffs’ energy value. The objective of this paper was to implement a multilayer perceptron network to estimate the nitrogen-corrected metabolisable energy (AMEn) values of the energetic and protein concentrate feeds, generally used by the poultry feed industry. The concentrate feeds were from plant origin. The dataset contains 568 experimental results, all from Brazil. This dataset was separated into two parts: one part with 454 data, which was used to train, and the other one with 114 data, which was used to evaluate the accuracy of each implemented network. The accuracy of the models was evaluated on the basis of their values of mean squared error, R2, mean absolute deviation, mean absolute percentage error and bias. The 7-5-3-1 model presented the highest accuracy of prediction. It was developed an Excel® AMEn calculator by using the best model, which provides a rapid and efficient way to predict the AMEn values of concentrate feedstuffs for broilers.

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Copyright
Corresponding author
E-mail: flaviaqz@gmail.com
References
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Ahmadi H, Mottaghitalab M, Nariman-Zadeh N 2007. Group method of data handling-type neural network prediction of broiler performance based on dietary metabolizable energy, methionine, and lysine. Journal of Applied Poultry Research 16, 494501.
Ahmadi H, Golian A, Mottaghitalab M, 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 87, 19091912.
Albuquerque VHC, Alexandria AR, Cortez PC, Tavares JMRS 2009. Evaluation of multilayer perceptron and self-organizing map neural network topologies applied on microstructure segmentation from metallographic images. NDT & E International 42, 644651.
Alvarenga RR, Rodrigues PB, Zangeronimo MG, Freitas RTF, Lima RR, Bertechini AG, Fassani EJ 2011. Energetic values of feedstuffs for broilers determined with in vivo assays and prediction equations. Animal Feed Science and Technology 168, 257266.
Balcean S, Ooghe H 2004. Alternative methodologies in studies on business failure: do they produce better results than the classical statistical methods? Working Paper of Faculty of Economics and Business Administration, Ghent University, Belgium, 40pp.
Bishop CM 1995. Neural networks for pattern recognition. Oxford University Press, Oxford, UK, 482pp.
Bolzan AC, Machado RAF, Piaia JCZ 2008. Egg hatchability prediction by multiple linear regression and artificial neural networks. Brazilian Journal of Poultry Science 10, 97102.
Brunelli SR, Pinheiro JW, Silva CA, Fonseca NAN, Oliveira DD, Cunha GE, Souza LFA 2006. Feeding increasing defatted corn germ meal levels to broiler chickens. Brazilian Journal of Animal Science 35, 13491358.
Clôba GM, Clôba LP, Saliby E 2002. Cooperação entre redes neurais artificiais e técnicas clássicas para previsão de demanda de uma série de vendas de cerveja na Austrália. Pesquisa Operacional 22, 345358.
Cybenko G 1988. Continuos valued neural network with two hidden layers are sufficient. Technical Report, Departament of Computer Science, Tufts University, Medford, MA, USA.
Fagard RH, Staessen JA, Thijs L 1996. Advantages and disadvantages of the meta-analysis approach. Journal of Hypertension 14 (suppl. 2), 913.
Gheyas IA, Smith LS 2011. A novel neural network ensemble architecture for time series forecasting. Neurocomputing 74, 38553864.
Haider A, Hanif MN 2009. Inflation forecasting in Pakistan using artificial neural networks. Pakistan economic and social review 47, 123138.
Haykin S 2007. Neural networks – a comprehensive foundation, 3rd edition. Prentice-Hall Inc., Upper Saddle River, NJ, USA.
Igel C, Hüsken M 2000. Improving the RPROP learning algorithm. Proceedings of Second International Symposium on Neural Computing NC 2000, 23–26 May, Berlin, Germany, pp. 115–21.
Lovatto PA, Lehnen CR, Andretta I, Carvalho AD, Hauschild L 2007. Meta analysis in scientific research: a methodological approach. Brazilian Journal of Animal Science 36 (suppl.), 285294.
Mariano FCMQ, Lima RR, Rodrigues PB, Alvarenga RR, Nascimento GAJ 2012. Prediction equations of energetic values of feedstuffs obtained using meta-analysis and principal components. Ciência Rural 42, 16341640.
Moreira I, Ribeiro CR, Furlan AC, Scapinello C, Kutschenko M 2002. Utilization of defatted corn germ meal on growing-finishing pigs feeding – digestibility and performance. Brazilian Journal of Animal Science 31, 22382246.
Nascimento GAJ, Rodrigues PB, Freitas RTF, Allaman IB, Lima RR, Reis Neto RV 2011. Prediction equations to estimate the AMEn values of protein feedstuffs for poultry utilizing meta-analysis. Brazilian Journal of Animal Science 40, 21722177.
Nascimento GAJ, Rodrigues PB, Freitas RTF, Bertechini AG, Lima RR, Pucci LEA 2009. Prediction equations to estimate the energy values of plant origin concentrate feeds for poultry utilizing the meta-analysis. Brazilian Journal of Animal Science 38, 12651271.
Okut H, Gianola D, Rosa GJM, Weigel KA 2011. Prediction of body mass index in mice using dense molecular markers and a regularized neural network. Genetical Research 93, 189201.
Perai AH, Moghaddam HN, Asadpour S, Bahrampour J, Mansoori GH 2010. A comparison of artificial neural networks with other statistical approaches for the prediction of true metabolizable energy of meat and bone meal. Poultry Science 89, 15621568.
Pereira BB 1999. Introduction to neural networks in statistics. Center of Multivariate Analysis, Technical Report, Pennsylvania State University, Pennsylvania, USA.
Rodrigues PB, Rostagno HS, Albino LFT, Gomes PC, Barboza WA, Santana RT 2001. Energy values of millet, corn and corn byproducts, determined with broilers and adult cockerels. Brazilian Journal of Animal Science 30, 17671777.
Rumelhart DE, Hinton GE, Williams RJ 1986. Learning internal representations by error propagation. In Paralled distributed processing: explorations in the microstructure of cognition, vol. 1: foundations (ed. DE Rumelhart and JL McClelland), pp. 318362. The MIT Press, Cambridge, MA.
Santos AM, Seixas JM, Pereira BB, Medronho RA 2005. Using artificial neural networks and logistic regression in the prediction of Hepatitis A. Revista Brasileira de Epidemiologia 8, 117126.
Sauvant D, Schmidely P, Daudin JJ, St-Pierre NR 2008. Meta-analyses of experimental data in animal nutrition. Animal 2, 12031214.
Wan HF, Chen W, Qi ZL, Peng P, Peng J 2009. Prediction of true metabolizable energy from chemical composition of wheat milling by-products for ducks. Poultry Science 88, 9297.
Wijayasekara D, Manic M, Sabharwall P, Utgikar V 2011. Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique. Nuclear Engineering and Design 241, 25492557.
Zhao F, Zhang HF, Hou SS, Zhang ZY 2008. Predicting metabolizable energy of normal corn from its chemical composition in adult pekin ducks. Poultry Science 87, 16031608.
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