Response surface and neural network models for performance of broiler chicks fed diets varying in digestible protein and critical amino acids from 11 to 17 days of age

Abstract

Central composite design (CCD; 5 levels and 4 factors), response surface methodology (RSM), and artificial neural network-genetic algorithm (ANN-GA) were used to evaluate the response of broiler chicks [ADG and feed conversion ratio (FCR)] to dietary standardized ileal digestible protein (dP), lysine (dLys), total sulfur amino acids (dTSAA), and threonine (dThr). A total of 84 battery brooder units of 5 birds each were assigned to 28 diets of CCD containing 5 levels of dP (18-22%), dLys (1.06-1.30%), dTSAA (0.81-1.01%), and dThr (0.66-0.86%) from 11 to 17 d of age. The experimental results of CCD were fitted with the quadratic and artificial neural network models. A ridge analysis (for RSM models) and a genetic algorithm (for ANN-GA models) were used to compute the optimal response for ADG and FCR. For both ADG and FCR, the goodness of fit in terms of R(2) and MS error corresponding to ANN-GA and RSM models showed a substantially higher accuracy of prediction for ANN models (ADG model: R(2) = 0.99; FCR model: R(2) = 0.97) compared with RSM models (ADG model: R(2) = 0.70; FCR model: R(2) = 0.71). The ridge maximum analysis on ADG and minimum analysis on FCR models revealed that the maximum ADG may be obtained with 18.5, 1.10, 0.89, and 0.73% dP, dLys, dTSAA, and dThr, respectively, in diet, and minimum FCR may be obtained with 19.44, 1.18, 0.90, and 0.75% of dP, dLys, dTSAA, and dThr, respectively, in diet. The optimization results of ANN-GA models showed the maximum ADG may be achieved with 19.93, 1.06, 0.90, and 0.76% of dP, dLys, dTSAA, and dThr, respectively, in diet, and minimum FCR may be achieved with 18.63, 1.26, 0.84, and 0.69% of dP, dLys, dTSAA, and dThr, respectively, in diet. The results of this study revealed that the platform of CCD (for conducting growth trials with minimum treatments), RSM model, and ANN-GA (for experimental data modeling and optimization) may be used to describe the relationship between dietary nutrient concentrations and broiler performance to achieve the optimal target.

Predicting In vitro Culture Medium Macro-Nutrients Composition for Pear Rootstocks Using Regression Analysis and Neural Network Models

“…600 and 450 data lines were used to train and test the network. Before training, the data set (input and output data) was normalized in the range of −1 to 1 so as to make simpler the problem for the network, to attain fast conjunction minimum mean square error, and to make sure that the fall of targets (output data) into the particular range of the new feed forward network can be recreated (Demuth et al, 2006; Gulati et al, 2010; Ahmadi and Golian, 2011). …”

Growth responses of breast and leg muscles to essential amino acids in broiler chicks

“…The extent of absolute t-value (|t-value|) for each model parameter may indicate the contribution of each term to the statistical fit. Therefore, the greater the magnitude of absolute t-value, the more significant the corresponding coefficient (Ahmadi and Golian, 2011;Mehri et al, 2012). In terms of BM, the linear (|t-value| = 17.78) and quadratic (|t-value| = 17.02) effects of dietary dLys had the largest contribution on the breast meat response in chicks, followed by interaction between dietary dLys and dMet.…”

Optimization of response surface and neural network models in conjugation with desirability function for estimation of nutritional needs of methionine, lysine, and threonine in broiler chickens

“…The growth data obtained from DOE may be analyzed by response surface methodology (Ghazaghi et al, 2012;Mehri et al, 2012), soft-computing artificial neural networks (ANN), or both (Ahmadi and Golian, 2011;Mehri, 2012). Because the nutritional experiments involve more than one response, determination of optimum conditions on the independent variables requires simultaneous consideration of all the responses.…”