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Development of artificial neural network models based on experimental data of response surface methodology to establish the nutritional requirements of digestible lysine, methionine, and threonine in broiler chicks.
MedLine Citation:
PMID:  23155041     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
An artificial neural network (ANN) approach was used to develop feed-forward multilayer perceptron models to estimate the nutritional requirements of digestible lysine (dLys), methionine (dMet), and threonine (dThr) in broiler chicks. Sixty data lines representing response of the broiler chicks during 3 to 16 d of age to dietary levels of dLys (0.88-1.32%), dMet (0.42-0.58%), and dThr (0.53-0.87%) were obtained from literature and used to train the networks. The prediction values of ANN were compared with those of response surface methodology to evaluate the fitness of these 2 methods. The models were tested using R(2), mean absolute deviation, mean absolute percentage error, and absolute average deviation. The random search algorithm was used to optimize the developed ANN models to estimate the optimal values of dietary dLys, dMet, and dThr. The ANN models were used to assess the relative importance of each dietary input on the bird performance using sensitivity analysis. The statistical evaluations revealed the higher accuracy of ANN to predict the bird performance compared with response surface methodology models. The optimization results showed that the maximum BW gain may be obtained with dietary levels of 1.11, 0.51, and 0.78% of dLys, dMet, and dThr, respectively. Minimum feed conversion ratio may be achieved with dietary levels of 1.13, 0.54, 0.78% of dLys, dMet, and dThr, respectively. The sensitivity analysis on the models indicated that dietary Lys is the most important variable in the growth performance of the broiler chicks, followed by dietary Thr and Met. The results of this research revealed that the experimental data of a response-surface-methodology design could be successfully used to develop the well-designed ANN for pattern recognition of bird growth and optimization of nutritional requirements. The comparison between the 2 methods also showed that the statistical methods may have little effect on the ideal ratios of dMet and dThr to dLys in broiler chicks using multivariate optimization.
Authors:
M Mehri
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Poultry science     Volume:  91     ISSN:  0032-5791     ISO Abbreviation:  Poult. Sci.     Publication Date:  2012 Dec 
Date Detail:
Created Date:  2012-11-16     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0401150     Medline TA:  Poult Sci     Country:  United States    
Other Details:
Languages:  eng     Pagination:  3280-5     Citation Subset:  IM    
Affiliation:
Animal Science Department, Faculty of Agriculture, and.
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