【作者】 李再山；

【导师】 王康宁；

【作者基本信息】 四川农业大学 ， 动物营养与饲料科学， 2011， 硕士

【摘要】 本试验旨在利用化学成分及表观代谢能(AME)结合化学成分和傅里叶近红外光谱(FNIRS)建立1～21日龄艾维茵肉鸡菜粕和棉粕的净能(NE)预测模型,并比较菜粕和棉粕两种样品单独建模与合并建模的预测效果。测定15个菜粕和15个棉粕样品的NE,NE为维持净能(NEm)与沉积净能(NEp)之和,NEm和NEp均用比较屠宰试验测定,NEm测定结合回归法,设自由采食、限饲30%、50%、70%4个采食梯度；NEp测定结合套算法,每个样品为一个处理；各个采食梯度、基础日粮、菜粕和棉粕样品处理组均设6个重复,每个重复2只鸡,试鸡均选用平均体重为97.3±4.0 g的7日龄健康艾维茵肉公鸡,试验期均为7天。测定各个菜粕和棉粕的常规化学成分含量,并根据测定的化学成分、AME与其NE进行两种样品单独与合并的线性回归分析。将各个菜粕和棉粕样品水分都调整为9～13%的5个水分梯度,并在此水分背景下建立两种样品单独与合并的FNIRS净能预测模型。结果如下：1～21日龄艾维茵肉鸡菜粕和棉粕样品的NE分别为4.72～7.22MJ/kg DM和4.73-7.08MJ/kg DM:AME结合化学成分建立菜粕、棉粕以及两种样品合并的最佳预测方程的R2为0.995、0.998、0.995,RSD为0.052、0.033、0.052 MJ/kg DM,优于只用化学成分建立的最佳预测方程,其R2为0.973、0.985、0.973,RSD为0.123、0.100、0.123 MJ/kg DM；5个水分背景下建立菜粕、棉粕以及两种样品合并的FNIRS净能预测模型,其校正决定系数(Rcal2)分别为0.99、0.99、0.96,校正标准差(RMSEE)分别为0.042、0.068、0.139MJ/kg；交叉验证决定系数(Rcv2)分别为0.98、0.99、0.89,交叉验证标准差(RMSECV)分别为0.089、0.082、0.219MJ/kg；预测标准差(RMSEP)分别为0.091、0.089、0.377MJ/kg。说明测定的1～21日龄艾维茵肉鸡菜粕和棉粕的NE值是准确的；化学成分、AME结合化学成分都可建立较好的菜粕和棉粕NE预测模型,但AME结合化学成分建模效果优于只用化学成分建模；用FNIRS可建立较好的菜粕和棉粕NE预测模型,其效果与AME结合化学成分建模相当；利用化学成分、AME结合化学成分建模时,菜粕和棉粕单独及两样品合并建模效果相当,无明显差异,而用FNIRS建模时,两样品单独建模优于其合并建模。更多还原

【Abstract】 This study was conducted to establish reliable prediction models for net energy (NE) of rapeseed meals(RSM) and cottonseed meals(CSM) based on chemical composition and apparent metabolic energy(AME) combined with chemical composition and Fourier near infrared spectroscopy (FNIRS) for Avian broilers aged from 1 to 21 days, and to compare the predictive models of the two samples separately and together.NE value of RSM and CSM was measured as the sum value of NE for maintenance (NEm) and NE for deposition (NEp). The NEm were measured by regression method with four treatments, which were group ad libitum to food, groups restricted feeding by 30%,50%,70%, respectively. The NEp of 15 RSM and 15 CSM were measured by the method of substitution, each of RSM and CSM was assigned as a treatment. Chicks in both trails were 7-d-old, with an average weight of of 97.3±4.0 g and randomly allotted into 36 treatments with six replications of two chicks. The experiments lasted 7 days. Proximate compositions of RSM and CSM were measured. Analyses of linear regression were carried out between NE and AME values, and chemical composition based on the two samples separately and together. Each sample with measured NE value was divided into five parts and the moisture contents of RSM and CSM were adjusted to 9%,10%,11%,12% and 13%, respectively, and the models of FNIRS were established based on the two samples separately and together The results showed as follows:the NE value of RSM and CSM for broilers aged from 1 to 21 days were from 4.72 to 7.22 MJ/kg DM and from 4.73 to 7.08 MJ/kg DM respectively.the R2 of the best regression equations for two samples separately and together based on the AME combined with chemical composition were 0.995,0.998 and 0.995, respectively, and the relative standard deviations (RSD) were were 0.052,0.033 and 0.052 MJ/kg, respectively, the R2 of the best regression equations based on chemical composition were 0.973,0.985 and 0.973, respectively, and the RSD were 0.123,0.100 and 0.123 MJ/kg, respectively, the reliable regression equations based on AME combined with chemical composition were better than the reliable regression equations only based on chemical composition; The coefficient of determination in calibration (R2cal) and root mean square error of calibration (RMSEE) of the predictive models of two samples separately and together were 0.99/0.042, 0.99/0.068,0.96/0.139 MJ/kg; The coefficient of determination in cross validation (R2CV) and root mean square error of cross validation (RMSECV) of the predictive models of two samples separately and togethe were 0.98/0.089,0.99/0.082, 0.89/0.219 MJ/kg; and the root mean square error of prediction (RMSEP) were 0.091, 0.089 and 0.377 MJ/kg respectively.The results indicated that the NE value of RSM and CSM were accurate and the reliable prediction models for NE of RSM and CSM were established based on chemical composition and AME combined with chemical composition and FNIRS, the reliable regression equations based on AME combined with chemical composition were better than the reliable regression equations only based on chemical composition, The accuracy of the predictive NE model by NIRS is similar to the best equation from AME combined with chemical composition,the prediction models for NE of the two samples separately and together established based on chemical composition and AME combined with chemical composition both were reliable,but the prediction models for NE of the two samples separately were better than their together established based on FNIRS.更多还原