动物营养学报 >

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

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

实验方法 Experimental Methods

玉米干酒糟及其可溶物有效能值估测模型中定标样品选择方法的研究

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  • 1. 中国农业科学院北京畜牧兽医研究所, 动物营养学国家重点实验室, 北京 100193;
    2. 新希望六和股份有限公司, 北京 100102
杨霞(1989-),女,湖南怀化人,硕士研究生,从事饲料养分生物学效价评定的研究。E-mail:yangxia0820@qq.com

收稿日期: 2016-10-01

  网络出版日期: 2017-04-14

基金资助

国家自然科学基金（30901037）；新希望六和股份有限公司合作项目（QGCHT1201403240001）

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

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  • 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

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摘要

本试验旨在探讨玉米干酒糟及其可溶物（DDGS）有效能值估测模型中定标样品的选择原则。从23个玉米DDGS样品（定义为全样品库）中按酶水解物能值（EHGE）相差0.21 MJ/kg左右的梯度选择9个定标玉米DDGS样品，定义为选择性样品库；将剩余的14个玉米DDGS样品定义为非选择性样品库。然后，比较选择性样品库与非选择性样品库化学成分含量及变异的差异，以及通过全样品库和选择性样品库分别建立其化学成分对EHGE之间的回归模型，比较根据回归模型计算得到的非选择性样品库EHGE的差异。结果表明，选择性样品库和非选择性样品库的玉米DDGS在粗蛋白质（CP）、粗灰分（Ash）、粗脂肪（EE）、粗纤维（CF）、中性洗涤纤维（NDF）、酸性洗涤纤维（ADF）含量及EHGE平均值上均无显著性差异（P>0.05），CP、Ash、EE、CF、ADF、NDF含量及EHGE变异的方差上均无显著性差异（P>0.05）。选择性样品库和非选择性样品库化学成分含量在第一、二主成分得分载荷分布上，选择性样品库中仅1个玉米DDGS样品未与非选择性样品库的分布范围重叠。以选择性样品库样品建立的EHGE预测模型为EHGE=（3 566+53.94×EE-32.68×NDF）×4.184/1 000（R2=0.798 1，RSD=0.43 MJ/kg）；以全样品库样品建立的预测模型为EHGE=（3 742+29.67×EE-29.71×NDF）×4.184/1 000（R2=0.535 0，RSD=0.44 MJ/kg）。由2个模型获得的非选择性样品库（n=14）玉米DDGS的EHGE计算值与其实测值的绝对残差平均值分别为0.47和0.33 MJ/kg，差异不显著（P>0.05）。综上所述，在玉米DDGS有效能值的估测建模中，以EHGE作为定标样品的选择依据是可行的。

关键词： 玉米DDGS; 酶水解物能值; 定标样品

本文引用格式

杨霞, 赵峰, 李珂, 党方昆, 张虎, 尹丽婷, 张宏福 . 玉米干酒糟及其可溶物有效能值估测模型中定标样品选择方法的研究[J]. 动物营养学报, 2017 , 29(4) : 1218 -1226 . DOI: 10.3969/j.issn.1006-267x.2017.04.017

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

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