论文检索
期刊
全部知识仓储预印本开放期刊机构
高级检索

基于体外仿生酶水解物能值和化学成分预测谷物饲料原料的鸡代谢能值OA北大核心CSTPCD

Prediction Equations of Chicken Metabolizable Energy Values for Grain Ingredients Based on in Vitro Simulated Enzymatic Hydrolysate Gross Energy Values and Chemical Composition

中文摘要英文摘要

[目的]利用单胃仿生消化系统测定小麦、稻谷及糙米原料的鸡酶水解物能值(EHGE),结合两种体内法(自由采食法(FF)和排空强饲法(TF))测定的小麦、稻谷及糙米原料的鸡代谢能值,建立基于酶水解物能值和原料化学成分的鸡代谢能值预测模型,为谷物原料鸡代谢能值快速预测提供参考.[方法]使用单胃仿生消化系统分别测定 3 种来源的小麦、稻谷及糙米共计 9 个样品的鸡酶水解物能值(EHGE),每个谷物样品设置 5 个重复,每个重复 1 根消化管.结合前期通过自由采食法和排空强饲法测定的同批次原料的鸡表观代谢能值(AME)和真代谢能值(TME),利用线性回归建立基于化学成分及酶水解物能值的 AME、TME 预测模型.[结果](1)在干物质基础下,3 种来源的小麦原料酶水解物能值分别为 14.46、14.63和14.80 MJ·kg-1;3种来源的稻谷原料酶水解物能值分别为12.52、13.59和13.40 MJ·kg-1;3种来源的糙米原料酶水解物能值分别为 14.74、15.10 和 15.23 MJ·kg-1.(2)粗灰分和中性洗涤纤维分别与FF法测定的AME和TME(AMEFF,TMEFF)和TF法测定的AME和TME(AMETF,TMETF)呈极显著的负相关(P<0.01);EHGE与AMEFF、AMETF、TMEFF、TMETF均呈极显著正相关关系(P<0.01),相关系数分别为 0.801、0.864、0.807 和 0.866.(3)相较于EHGE建立的代谢能预测模型,基于化学成分建立的预测模型有更高的决定系数(R 2)、更低的残差标准差(RSD).对于AMEFF和TMEFF,Ash为最佳预测因子,预测模型为:AMEFF=16.728-0.842×Ash(R2=0.809,RSD=0.826,P=0.001),TMEFF=16.812-0.842×Ash(R 2=0.816,RSD=0.806,P=0.001);对于AMETF和TMETF,NDF为最佳预测因子,预测模型分别为:AMETF=16.106-0.157×NDF(R 2=0.907,RSD=0.523,P<0.001),TMETF=17.654-0.157×NDF(R 2=0.903,RSD=0.534,P<0.001).[结论]小麦、和糙米的EHGE高于稻谷 EHGE,且三类谷物原料的EHGE与体内法测定的代谢能值具有较好的相关性.本研究条件下,基于化学成分建立的谷物AME和TME预测模型优于基于EHGE建立的预测模型.

[Objective]This study aimed to measure the enzymatic hydrolysate gross energy(EHGE)of chicken for wheat,paddy,and brown rice ingredients using a monogastric simulated digestion system,and it also aimed to correlate these measurements with the chemical composition of these ingredients.Moreover,the study sought to establish predictive equations for chicken metabolizable energy values based on the EHGE values and the grain ingredients'chemical compositions.The findings would provide a reference for the rapid prediction of the metabolizable energy value of grain ingredients for chickens.[Method]The EHGE values of nine samples from three sources of wheat,paddy,and brown rice ingredients were measured.Five replicates were set for each grain sample,with one digestion tube per replicate.The apparent metabolizable energy values(AME)and true metabolizable energy values(TME)of the same batch of ingredients were also measured by the free feeding method(FF)and the tube-feeding method(TF).A linear regression model was then used to establish predictive equations for AME and TME based on chemical composition and EHGE values.[Result](1)Based on dry matter basis,the EHGE values of wheat,paddy,and brown rice from the three sources were 14.46,14.63,and 14.80 MJ·kg-1;12.52,13.59,13.40 MJ·kg-1,and 14.74,15.10,15.23 MJ·kg-1,respectively.(2)Ash and neutral detergent fiber exhibited a negative correlation with AME(AMEFF and TMEFF)and TME(AMETF and TMETF)measured by both FF and TF methods(P<0.01).EHGE exhibited a significant positive correlation with AMEFF,TMEFF,AMETF,and TMET measured by both methods(P<0.01),with correlation coefficients of 0.801,0.864,0.807,and 0.866,respectively.(3)Compared with the metabolizable energy prediction equations established by EHGE,the prediction equations based on chemical composition had higher coefficients of determination(R2)and lower residual standard deviations(RSD).For AMEFF and TMEFF,Ash was the best predictor,with prediction equations:AMEFF=16.728-0.842×Ash(R2=0.809,RSD=0.826,P=0.001),and TMEFF=16.812-0.842×Ash(R2=0.816,RSD=0.806,P=0.001).On the other hand,for the AMETF and TMETF variables,NDF was identified as the best predictor.The prediction equations for AMETF and TMETF were AMETF=16.106-0.157×NDF(R2=0.907,RSD=0.523,P<0.001),and TMETF=17.654-0.157×NDF(R2=0.903,RSD=0.534,P<0.001),respectively.[Conclusion]The EHGE of wheat and brown rice was higher than that of paddy,and there was a good correlation between the EHGE values of the three grain ingredients and the metabolizable energy values measured by the in vivo method.The prediction model for the AME and TME of grains based on chemical composition was superior to the prediction model based on EHGE.

李凯;白国松;滕春然;马腾;钟儒清;陈亮;张宏福

中国农业科学院北京畜牧兽医研究所/畜禽营养与饲养全国重点实验室,北京 100193

鸡;代谢能;酶水解物能值;预测模型;谷物

chicken;metabolizable energy;enzymatic hydrolysate gross energy;prediction equation;grain

《中国农业科学》 2024 (010)

2035-2045 / 11

国家重点研发计划(2022YFD1300605)、公益性科研院所基本科研业务费专项(2023-YWF-ZX-03)、现代农业产业技术体系专项资金(CARS-41)

10.3864/j.issn.0578-1752.2024.10.014

评论

下载量:0
点击量:0