Prediction of apparent metabolizable energy and metabolizable energy corrected for nitrogen of corn according to physical classification of the grain

ABSTRACT The objective of this study was to develop an equation to determine the apparent metabolizable energy (AME) and metabolizable energy corrected for nitrogen balance (AMEn) using a physical-based classification of corn. A total of 5,055 samples were taken from bulk cargo trucks, over a five-year period. The parameters studied were the variables related to the physical characteristics of grains. The density of maize was evaluated, and AME and AMEn were calculated. The average value for AME was 3,375 kcal/kg, and two groups were formed of high quality and low quality for all samples. Stepwise regression analysis was then carried out using grain quality to estimate AME and AMEn, and the validation of the equations was carried out with 6,490 independent samples. The average value for density was 767.7 kg/m3. The multiple regressions used to estimate AME and AMEn as a function of humidity, density, and physical composition of corn kernels showed that moisture was included for AME, but not for AMEn. The equations presented high coefficients of determination (R2) for AME (0.994) and AMEn (0.987). The discriminant analyses correctly classified 98% of the high-quality samples and 96.69% of low-quality samples, so the error was smaller than the expected. The calculated equations were shown to be good at discriminating between samples of high and low quality of corn according to its physical composition, and the most important variables for separation between groups were damaged grain fraction, impurities, burnt, and soft. The correlation between calculated (independent samples) and estimated metabolizable energy and AMEn were, respectively, 0.9942 and 0.9859. The corn energy values can be estimated based on physical evaluation of the grain.摘要 本研究旨在基于玉米的物理性状分类方法，建立可测定表观代谢能（apparent metabolizable energy, AME）与氮平衡校正代谢能（metabolizable energy corrected for nitrogen balance, AMEn）的预测方程。本研究于5年周期内，从散装货运卡车中采集总计5055份玉米样本，选取与谷物物理特性相关的变量作为分析参数，测定了玉米籽粒密度并计算了样本的AME与AMEn值。样本的AME平均值为3375 kcal/kg，并以此为依据将所有样本划分为高品质与低品质两个组别。随后以籽粒品质相关变量为自变量开展逐步回归分析（stepwise regression analysis），以估算AME与AMEn，并使用6490份独立样本对所建立的预测方程进行验证。样本的平均籽粒密度为767.7 kg/m³。以玉米籽粒的湿度、密度及物理组分为自变量建立的多元回归模型显示，湿度被纳入AME的估算方程，但未被纳入AMEn的估算方程。所建立的方程对AME与AMEn均展现出较高的决定系数（coefficient of determination, R²），分别为0.994与0.987。判别分析（discriminant analyses）可正确分类98%的高品质样本与96.69%的低品质样本，分类误差低于预期水平。结果表明，所建立的计算方程可有效基于玉米物理组分区分高品质与低品质玉米样本，其中用于组间区分的核心变量为破损籽粒组分、杂质、焦糊籽粒与软质籽粒。使用独立样本计算得到的实测代谢能与AMEn值，与模型估算值之间的相关系数分别为0.9942与0.9859。综上，可通过对玉米籽粒的物理性状评估来准确估算其能量值。