Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, DK 8830 Tjele, Denmark.
Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, DK 8830 Tjele, Denmark; Animal Breeding and Genomics, Wageningen University and Research, NL 6700 AH Wageningen, the Netherlands.
J Dairy Sci. 2017 Nov;100(11):9052-9060. doi: 10.3168/jds.2017-13014. Epub 2017 Sep 13.
Enteric methane (CH), a potent greenhouse gas, is among the main targets of mitigation practices for the dairy industry. A measurement technique that is rapid, inexpensive, easy to use, and applicable at the population level is desired to estimate CH emission from dairy cows. In the present study, feasibility of milk Fourier transform mid-infrared (FT-IR) spectral profiles as a predictor for CH:CO ratio and CH production (L/d) is explained. The partial least squares regression method was used to develop the prediction models. The models were validated using different random test sets, which are independent from the training set by leaving out records of 20% cows for validation and keeping records of 80% of cows for training the model. The data set consisted of 3,623 records from 500 Danish Holstein cows from both experimental and commercial farms. For both CH:CO ratio and CH production, low prediction accuracies were found when models were obtained using FT-IR spectra. Validated coefficient of determination (R) = 0.21 with validated model error root mean squared error of prediction (RMSEP) = 0.0114 L/d for CH:CO ratio, and R = 0.13 with RMSEP = 111 L/d for CH production. The important spectral wavenumbers selected using the recursive partial least squares method represented major milk components fat, protein, and lactose regions of the spectra. When fat and protein predicted by FT-IR were used instead of full spectra, a low R of 0.07 was obtained for both CH:CO ratio and CH production prediction. Other spectral wavenumbers related to lactose (carbohydrate) or additional wavenumbers related to fat or protein (amide II) are providing additional variation when using the full spectral profile. For CH:CO ratio prediction, integration of FT-IR with other factors such as milk yield, herd, and lactation stage showed improvement in the prediction accuracy. However, overall prediction accuracy remained modest; R increased to 0.31 with RMSEP = 0.0105. For prediction of CH production, the added value of FT-IR along with the aforementioned traits was marginal. These results indicated that for CH production prediction, FT-IR profiles reflect primarily information related to milk yield, herd, and lactation stage rather than individual milk fatty acids related to CH emission. Thus, it is not feasible to predict CH emission based on FT-IR spectra alone.
肠道甲烷（CH）是一种强效的温室气体，是奶制品行业减排的主要目标之一。人们希望有一种快速、廉价、易于使用且适用于群体水平的测量技术来估算奶牛的 CH 排放量。本研究解释了利用牛奶傅里叶变换中红外（FT-IR）光谱曲线预测 CH：CO 比和 CH 产量（L/d）的可行性。采用偏最小二乘回归法建立预测模型。利用不同的随机测试集对模型进行验证，这些测试集通过剔除 20%奶牛的记录来与训练集保持独立，并用 80%奶牛的记录来训练模型。该数据集由来自丹麦荷斯坦奶牛的 500 头奶牛的 3623 条记录组成，这些奶牛来自实验农场和商业农场。对于 CH：CO 比和 CH 产量，使用 FT-IR 光谱获得的模型预测精度较低。CH：CO 比的验证决定系数（R）为 0.21，验证预测均方根误差（RMSEP）为 0.0114 L/d；CH 产量的 R 为 0.13，RMSEP 为 111 L/d。使用递归偏最小二乘法选择的重要光谱波数代表了光谱中乳脂、乳蛋白和乳糖的主要乳成分区域。当使用 FT-IR 预测的乳脂和乳蛋白代替全光谱时，CH：CO 比和 CH 产量的预测 R 值均较低，分别为 0.07。与乳糖（碳水化合物）相关的其他光谱波数或与乳脂或乳蛋白（酰胺 II）相关的其他波数在使用全光谱时提供了更多的变化。对于 CH：CO 比的预测，FT-IR 与产奶量、牛群和泌乳阶段等其他因素的结合提高了预测精度。然而，整体预测精度仍然不高；R 值增加到 0.31，RMSEP = 0.0105。对于 CH 产量的预测，FT-IR 与上述特征的结合附加值较小。这些结果表明，对于 CH 产量的预测，FT-IR 谱主要反映与产奶量、牛群和泌乳阶段相关的信息，而不是与 CH 排放相关的单个乳脂肪酸的信息。因此，仅基于 FT-IR 光谱预测 CH 排放是不可行的。
