Method of Selecting Calibration Samples to Establish Prediction Model for Effective Energy Values of Corn Dried Distiller's Grains with Solubles

YANG Xia, ZHAO Feng, LI Ke, DANG Fangkun, ZHANG Hu, YIN Liting, ZHANG Hongfu

doi:10.3969/j.issn.1006-267x.2017.04.017

2017 , Vol. 29 >Issue 4: 1218 - 1226

DOI: https://doi.org/10.3969/j.issn.1006-267x.2017.04.017

Molecular Nutrition

Method of Selecting Calibration Samples to Establish Prediction Model for Effective Energy Values of Corn Dried Distiller's Grains with Solubles

Expand

1. State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China;
2. New Hope Liuhe Co., Ltd., Beijing 100102, China

Received date: 2016-10-01

Online published: 2017-04-14

Fold

Abstract

The objective of this study was to investigate the rule of selecting calibration samples to establish prediction model for the effective energy of corn dried distiller's grains with solubles (DDGS). Nine corn DDGS samples were selected from 23 corn DDGS samples (defined as full sample pool) according to the enzymatic hydrolyzate gross energy (EHGE) values with an interval of about 0.21 MJ/kg and defined as a selected sample pool. The remaining 14 corn DDGS samples were defined as a non-selected sample pool. The content and variation of chemical composition and EHGE values were compared between selected sample pool and non-selected sample pool. The regression models to predict EHGE from chemical composition were established based on full sample pool and selected sample pool, respectively. The non-selected sample pool was used to compare the values of EHGE calculated on the models based on full sample pool and selected sample pool. The results indicated that no significant difference was observed on the content of crude protein (CP), crude ash (Ash), ether extract (EE), crude fiber (CF), neutral detergent fiber (NDF) and acid detergent fiber (ADF) and EHGE between the corn DDGS from selected samples pool and non-selected pool (P>0.05). Also, no significant difference was observed on variance in CP, Ash, EE CF, NDF, ADF and EHGE between the corn DDGS from selected samples pool and non-selected pool (P>0.05). The principal component analysis showed an enormous overlapping on the score plot of principal 1 and principal 2 of selected sample pool and non-selected sample pool excluding 1 corn DDGS sample. The regression model to predict EHGE was EHGE=(3 566+53.94×EE-32.68×NDF)×4.184/1 000 (R²=0.798 1, RSD=0.43 MJ/kg) for selected sample pool and EHGE=(3 742+29.67×EE-29.71×NDF)×4.184/1 000 (R²=0.535 0, RSD=0.44 MJ/kg) for full sample pool, respectively. The mean absolute residuals of calculated value and measured value of predicted EHGE of non-selected pool (n=14) were 0.47 and 0.33 MJ/kg, respectively, and no significant difference was observed (P>0.05). In conclusion, it is practicable to use EHGE to select calibration samples for establishing prediction models of effective energy in corn DDGS.

Key words： corn DDGS; enzymatic hydrolyzate gross energy; calibration samples

Cite this article

YANG Xia, ZHAO Feng, LI Ke, DANG Fangkun, ZHANG Hu, YIN Liting, ZHANG Hongfu . Method of Selecting Calibration Samples to Establish Prediction Model for Effective Energy Values of Corn Dried Distiller's Grains with Solubles[J]. Chinese Journal of Animal Nutrition, 2017 , 29(4) : 1218 -1226 . DOI: 10.3969/j.issn.1006-267x.2017.04.017

References

[1] 何锡文,郭薇.化学计量——取样学最近的进展[J].分析化学,1995,23(12):1455-1461.

[2] 李婷婷,蔡辉益,闫海洁,等.玉米干酒糟及其可溶物家禽表观代谢能预测模型[J].动物营养学报,2014,26(6):1556-1562.

[3] ROCHELL S J,KERR B J,DOZIER W A.Energy determination of corn co-products fed to broiler chicks from 15 to 24 days of age,and use of composition analysis to predict nitrogen-corrected apparent metabolizable energy[J].Poultry Science,2011,90(9):1999-2007.

[4] CARRÉ B.Predicting the dietary energy value of poultry feeds[C]//WISEMAN J,COLE D J A.Feedstuff Evaluation.Guildford:Butterworths,1990:283-300.

[5] ZHAO F,REN L Q,MI B M,et al.Developing a computer-controlled simulated digestion system to predict the concentration of metabolizable energy of feedstuffs for rooster[J].Journal of Animal Science,2014,92(4):1537-1547.

[6] URRIOLA P E,LI M,KERR B J,et al.Evaluation of prediction equations to estimate gross,digestible,and metabolizable energy content of maize dried distillers grains with solubles (DDGS) for swine based on chemical composition[J].Animal Feed Science and Technology,2014,198:196-202.

[7] 袁志发,周静芋.试验设计与分析[M].北京:高等教育出版社,2000.

[8] DALE N M,FULLER H L.Repeatability of true metabolizable energy versus nitrogen corrected true metabolizable energy values[J].Poultry Science,1986,65(2):352-354.

[9] 中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会.GB/T 6435-2006饲料中水分和其他挥发性物质含量的测定[S].北京:中国标准出版社,2007.

[10] 国家技术监督局.GB/T 6432-1994饲料中粗蛋白测定方法[S].北京:中国标准出版社,1994.

[11] 中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会.GB/T 6438-2007饲料中粗灰分的测定[S].北京:中国标准出版社,2007.

[12] 中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会.GB/T 6433-2006饲料中粗脂肪的测定[S].北京:中国标准出版社,2006.

[13] 中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会.GB/T 6434-2006饲料中粗纤维的含量测定过滤法[S].北京:中国标准出版社,2006.

[14] 中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会.GB/T 20806-2006饲料中中性洗涤纤维(NDF)的测定[S].北京:中国标准出版社,2007.

[15] 中华人民共和国农业部.NY/T 1459-2007饲料中酸性洗涤纤维的测定[S].北京:农业出版社,2008.

[16] 赵峰,张宏福,张子仪.单胃动物仿生消化系统操作手册[M].2版.北京:中国农业科学院,2011.

[17] YEGANI M,SWIFT M L,ZIJLSTRA R T,et al.Prediction of energetic value of wheat and triticale in broiler chicks:a chick bioassay and an in vitro digestibility technique[J].Animal Feed Science and Technology,2013,183(1/2):40-50.

[18] 芦永军,曲艳玲,朴仁官,等.近红外光谱分析技术定标和预测中的相似样品剔除算法[J].光谱学与光谱分析,2004,24(2):158-161.

[19] 中华人民共和国国家质量监督检验检疫总局.GB/T 18868-2002饲料中水分、粗蛋白质、粗纤维、粗脂肪、赖氨酸、蛋氨酸快速测定近红外光谱法[S].北京:中国标准出版社,2003.

[20] 赵峰.用酶法评定鸭饲料代谢能的方法学研究[D].博士学位论文.北京:中国农业科学院,2006.

[21] MELOCHE K J,KERR B J,SHURSON G C,et al.Apparent metabolizable energy and prediction equations for reduced-oil corn distillers dried grains with solubles in broiler chicks from 10 to 18 days of age[J].Poultry Science,2013,92(12):3176-3183.

[22] PEDERSEN M B,DALSGAARD S,KNUDSEN K E B,et al.Compositional profile and variation of distillers dried grains with solubles from various origins with focus on non-starch polysaccharides[J].Animal Feed Science and Technology,2014,197:130-141.

[23] GALLO A,MOSCHINI M,CERIOLI C,et al.Use of principal component analysis to classify forages and predict their calculated energy content[J].Animal,2013,7(6):930-939.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References