Center of Excellence in the Animal Sciences Department, Ferdowsi University of Mashhad, Mashhad, Iran.
Poult Sci. 2011 Sep;90(9):2085-96. doi: 10.3382/ps.2011-01367.
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.
采用中心组合设计（CCD；5 个水平和 4 个因素）、响应面法（RSM）和人工神经网络-遗传算法（ANN-GA）来评估肉鸡[ADG 和饲料转化率（FCR）]对日粮可消化真蛋白（dP）、赖氨酸（dLys）、总硫氨基酸（dTSAA）和苏氨酸（dThr）的反应。共有 84 个电池育雏器，每个育雏器有 5 只鸡，分为 28 个 CCD 日粮，11-17 日龄时日粮中 dP（18-22%）、dLys（1.06-1.30%）、dTSAA（0.81-1.01%）和 dThr（0.66-0.86%）有 5 个水平。CCD 的实验结果与二次和人工神经网络模型拟合。使用岭分析（用于 RSM 模型）和遗传算法（用于 ANN-GA 模型）计算 ADG 和 FCR 的最佳反应。对于 ADG 和 FCR，ANN-GA 和 RSM 模型的 R（2）和 MS 误差表示拟合优度，ANN 模型的预测精度明显高于 RSM 模型（ADG 模型：R（2）=0.99；FCR 模型：R（2）=0.97）。ADG 的岭最大分析和 FCR 模型的最小分析表明，日粮中 dP、dLys、dTSAA 和 dThr 的最大 ADG 可能分别为 18.5%、1.10%、0.89%和 0.73%，最小 FCR 可能分别为 19.44%、1.18%、0.90%和 0.75%。ANN-GA 模型的优化结果表明，日粮中 dP、dLys、dTSAA 和 dThr 的最大 ADG 可能分别为 19.93%、1.06%、0.90%和 0.76%，最小 FCR 可能分别为 18.63%、1.26%、0.84%和 0.69%。这项研究的结果表明，CCD 平台（用于进行最少处理的生长试验）、RSM 模型和 ANN-GA（用于实验数据建模和优化）可用于描述日粮营养浓度与肉鸡性能之间的关系，以达到最佳目标。
