Ferragina A, Cipolat-Gotet C, Cecchinato A, Pazzola M, Dettori M L, Vacca G M, Bittante G
Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE) University of Padova, viale dell'Università 16 - 35020 Legnaro (PD), Italy.
Department of Veterinary Medicine, University of Sassari, Via Vienna 2, 07100 Sassari, Italy.
J Dairy Sci. 2017 May;100(5):3526-3538. doi: 10.3168/jds.2016-12226. Epub 2017 Mar 16.
The aim of this study was to apply Bayesian models to the Fourier-transform infrared spectroscopy spectra of individual sheep milk samples to derive calibration equations to predict traditional and modeled milk coagulation properties (MCP), and to assess the repeatability of MCP measures and their predictions. Data consisted of 1,002 individual milk samples collected from Sarda ewes reared in 22 farms in the region of Sardinia (Italy) for which MCP and modeled curd-firming parameters were available. Two milk samples were taken from 87 ewes and analyzed with the aim of estimating repeatability, whereas a single sample was taken from the other 915 ewes. Therefore, a total of 1,089 analyses were performed. For each sample, 2 spectra in the infrared region 5,011 to 925 cm were available and averaged before data analysis. BayesB models were used to calibrate equations for each of the traits. Prediction accuracy was estimated for each trait and model using 20 replicates of a training-testing validation procedure. The repeatability of MCP measures and their predictions were also compared. The correlations between measured and predicted traits, in the external validation, were always higher than 0.5 (0.88 for rennet coagulation time). We confirmed that the most important element for finding the prediction accuracy is the repeatability of the gold standard analyses used for building calibration equations. Repeatability measures of the predicted traits were generally high (≥95%), even for those traits with moderate analytical repeatability. Our results show that Bayesian models applied to Fourier-transform infrared spectra are powerful tools for cheap and rapid prediction of important traits in ovine milk and, compared with other methods, could help in the interpretation of results.
本研究的目的是将贝叶斯模型应用于单个绵羊奶样的傅里叶变换红外光谱，以推导校准方程来预测传统和模拟的牛奶凝固特性（MCP），并评估MCP测量及其预测的可重复性。数据包括从意大利撒丁岛地区22个农场饲养的撒丁岛母羊采集的1002个个体奶样，这些奶样的MCP和模拟凝乳形成参数是可用的。从87只母羊身上采集了两份奶样进行分析以估计可重复性，而从其他915只母羊身上只采集了一份奶样。因此，总共进行了1089次分析。对于每个样本，在数据分析前可获得红外区域5011至925 cm的2个光谱并进行平均。使用贝叶斯B模型对每个性状的方程进行校准。使用训练-测试验证程序的20次重复对每个性状和模型估计预测准确性。还比较了MCP测量及其预测的可重复性。在外部验证中，测量性状与预测性状之间的相关性始终高于0.5（凝乳酶凝固时间为0.88）。我们证实，找到预测准确性的最重要因素是用于建立校准方程的金标准分析的可重复性。预测性状的可重复性测量通常较高（≥95%），即使对于那些分析可重复性中等的性状也是如此。我们的结果表明，应用于傅里叶变换红外光谱的贝叶斯模型是廉价快速预测羊奶重要性状的有力工具，与其他方法相比，有助于结果的解释。
