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Prediction Model for True Metabolizable Energy of Feather Meal and Poultry Offal Meal Using Group Method of Data Handling-Type Neural Network

H. Ahmadi^*,1, A. Golian^*, M. Mottaghitalab and N. Nariman-Zadeh

^* Center of Excellence in the Animal Science Department, Ferdowsi University of Mashhad, Mashhad, Iran 91775-1163; Department of Animal Science, University of Guilan, Rasht, Iran 41635-1314; and Department of Mechanical Engineering, University of Guilan, Rasht, Iran 41635-3756

¹ Corresponding author: hahmadima{at}yahoo.com

A group method of data handling-type neural network (GMDH-type NN) with an evolutionary method of genetic algorithm was used to predict the TME_n of feather meal (FM) and poultry offal meal (POM) based on their CP, ether extract, and ash content. Thirty-seven data lines consisting of 15 FM and 22 POM samples were collected from literature and used to train a GMDH-type NN model. A genetic algorithm was deployed to design the whole architecture of the GMDH-type NN. The accuracy of the model was examined by R² value, adjusted R², mean square error, residual standard deviation, mean absolute percentage error, and bias. The developed model could accurately predict the TME_n of FM or POM samples from their chemical composition. The R² for the GMDH-type NN model had a higher accuracy of prediction than 2 models reported previously. This study revealed that the novel modeling of GMDH-type NN with method of genetic algorithm can be used to predict the TME_n of poultry by-products.

Key Words: feather meal • poultry offal meal • metabolizable energy • neural network model