Department of Animal Sciences, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil.
Department of Chemistry, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil.
PLoS One. 2024 Apr 18;19(4):e0296447. doi: 10.1371/journal.pone.0296447. eCollection 2024.
The aim of this study was to develop and validate regression models to predict the chemical composition and ruminal degradation parameters of corn silage by near-infrared spectroscopy (NIR). Ninety-four samples were used to develop and validate the models to predict corn silage composition. A subset of 23 samples was used to develop and validate models to predict ruminal degradation parameters of corn silage. Wet chemistry methods were used to determine the composition values and ruminal degradation parameters of the corn silage samples. The dried and ground samples had their NIR spectra scanned using a poliSPECNIR 900-1700 model NIR sprectrophotometer (ITPhotonics S.r.l, Breganze, IT.). The models were developed using regression by partial least squares (PLS), and the ordered predictor selection (OPS) method was used. In general, the regression models obtained to predict the corn silage composition (P>0.05), except the model for organic matter (OM), adequately estimated the studied properties. It was not possible to develop prediction models for the potentially degradable fraction in the rumen of OM and crude protein and the degradation rate of OM. The regression models that could be obtained to predict the ruminal degradation parameters showed correlation coefficient of calibration between 0.530 and 0.985. The regression models developed to predict CS composition accurately estimated the CS composition, except the model for OM. The NIR has potential to be used by nutritionists as a rapid prediction tool for ruminal degradation parameters in the field.
本研究旨在利用近红外光谱（NIR）开发和验证预测玉米青贮化学成分和瘤胃降解参数的回归模型。使用 94 个样本建立和验证预测玉米青贮化学成分的模型，使用 23 个样本子集建立和验证预测玉米青贮瘤胃降解参数的模型。使用湿法化学方法测定玉米青贮样品的化学成分值和瘤胃降解参数。将干燥和研磨的样品用 poliSPECNIR 900-1700 型号近红外分光光度计（ITPhotonics S.r.l，布雷甘扎，意大利）扫描其 NIR 光谱。使用偏最小二乘（PLS）回归法和有序预测选择（OPS）法建立模型。一般来说，除了预测有机物（OM）的模型外，用于预测玉米青贮化学成分的回归模型（P＞0.05）能够充分估计所研究的特性。无法为 OM 和粗蛋白的瘤胃可降解部分以及 OM 的降解率建立预测模型。可以获得用于预测瘤胃降解参数的回归模型，其校准相关系数在 0.530 到 0.985 之间。用于预测 CS 化学成分的回归模型能够准确地估计 CS 化学成分，除了预测 OM 的模型。NIR 有潜力成为营养师在现场快速预测瘤胃降解参数的工具。
