摘要
本试验旨在建立合理的1-21日龄黄羽肉鸡豆粕净能（NE）预测模型。对豆粕净能进行测定，为维持净能（NEm）与沉积净能（NEp）之和，NEm测定采用回归法，NEp测定采用套算法；测定21个豆粕样品的概略养分含量，并进行NE与表观代谢能（AME）、化学成分的一元或多元线性回归分析，建立模型；将21个豆粕样品水分分别调整为11%、12%和13%，并分别建立3种水分及全局傅里叶近红外光谱（FNIRS）模型。结果表明：1）1~21日龄黄羽肉鸡21种豆粕NE为6.045~7.829 MJ/kg，AME转化为NE的效率为55.24%-62.78%；2）用化学成分建立的最佳豆粕NE预测方程的R2为0.96，RSD为0.114 MJ/kg；用AME结合化学成分建立的最佳预测方程的R2为0.98，RSD为0.079 MJ/kg；3）3个水分以及全局FNIRS模型校正相关系数（R2cal）分别为0.96、0.98、0.97、0.94，校正标准差（RMSEE）分别为0.100、0.072、0.069、0.105 MJ/kg;交叉验证相关系数（R2cv）分别为0.92、0.95、0.95、0.93；交叉验证标准差（RMSECV）分别为0.131、0.096、0.089和0.116 MJ/kg。结果提示，通过调节水分扩大样本可建立可靠、方便的豆粕NE的全局FNIRS模型，FNIRS模型与只用化学成分建立的预测模型准确度相当，较用AME结合化学成分建立预测模型的准确度稍差。
The study was conducted to establish reliable prediction models for net energy（NE） of soybean meals（SM） for yellow-feathered broilers aged from 1 to 21 days.NE value of SM was measured as the sum value of NE for maintenance（NEm） and NE for deposition（NEp）.NEm and NEp were determined by regression method and substitution method,respectively.Proximate compositions of 21 SM samples were measured.Analyses of simple and multiple linear regression were carried out between NE and apparent metabolic energy（AME） values,and chemical composition.The moisture contents of 21 samples were adjusted to 11%,12% and 13%,respectively,and the model of Fourier near infrared spectroscopy（FNIRS） was established based on the three moisture and the global.The results showed as follows： 1） the NE value of 21 SM samples for broilers aged from 1 to 21 days were from 6.045 to 7.829 MJ/kg,and the conversion efficiencies of AME to NE were from 55.24% to 62.78%;2） the correlation coefficients（R2） of the best regression equations based on chemical composition and AME combined with chemical composition were 0.96 and 0.98,respectively,and the relative standard deviations（RSD） were 0.114 and 0.079 MJ/kg,respectively;3） the correlation coefficients in calibration（R2cal） of the FNIRS models were 0.96,0.98,0.97 and 0.94,respectively,and the root mean square errors of calibration（RMSEE） were 0.100,0.072,0.069,0.105 MJ/kg,respectively;the correlation coefficients in cross validation（R2cv） were 0.92,0.95,0.95 and 0.93,respectively,and the root mean square error of cross validation（RMSECV） were 0.131,0.096,0.089 and 0.116 MJ/kg,respectively.The results indicate that a reliable and convenient NIRS model of NE value can be established by enlarging the sample size with the adjusting of moisture.The accuracy of FNIRS model is as high as the model based on chemical composition,but less than that of the model based on AME combined with chemical composition.
出处
《动物营养学报》
CAS
CSCD
北大核心
2011年第2期250-257,共8页
CHINESE JOURNAL OF ANIMAL NUTRITION
基金
四川农业大学双支计划
关键词
黄羽肉鸡
净能
傅里叶近红外光谱
全局校正模型
yellow-feathered broilers
net energy
Fourier near infrared spectroscopy
global calibration model
作者简介
张正帆（1985-），男，四川简阳人，硕士研究生，主要从事单胃动物营养研究。E—mail：zhangzhengfan@163．com
通讯作者：王康宁，研究员，博士生导师，E—mail：wkn@sicau．edu．cn
