Department of Veterinary Science, University of Parma, 43126 Parma, Italy.
Department of Veterinary Science, University of Parma, 43126 Parma, Italy.
J Dairy Sci. 2021 Apr;104(4):3927-3935. doi: 10.3168/jds.2020-19587. Epub 2021 Feb 13.
Driven by the large amount of goat milk destined for cheese production, and to pioneer the goat cheese industry, the objective of this study was to assess the effect of farm in predicting goat milk-coagulation and curd-firmness traits via Fourier-transform infrared spectroscopy. Spectra from 452 Sarda goats belonging to 14 farms in central and southeast Sardinia (Italy) were collected. A Bayesian linear regression model was used, estimating all spectral wavelengths' effects simultaneously. Three traditional milk-coagulation properties [rennet coagulation time (min), time to curd firmness of 20 mm (min), and curd firmness 30 min after rennet addition (mm)] and 3 curd-firmness measures modeled over time [rennet coagulation time estimated according to curd firmness change over time (RCT), instant curd-firming rate constant, and asymptotical curd firmness] were considered. A stratified cross validation (SCV) was assigned, evaluating each farm separately (validation set; VAL) and keeping the remaining farms to train (calibration set) the statistical model. Moreover, a SCV, where 20% of the goats randomly taken (10 replicates per farm) from the VAL farm entered the calibration set, was also considered (SCV). To assess model performance, coefficient of determination (R) and the root mean squared error of validation were recorded. The R varied between 0.14 and 0.45 (instant curd-firming rate constant and RCT, respectively), albeit the standard deviation was approximating half of the mean for all the traits. Although average results of the 2 SCV procedures were similar, in SCV, the maximum R increased at about 15% across traits, with the highest observed for time to curd firmness of 20 mm (20%) and the lowest for RCT (6%). Further investigation evidenced important variability among farms, with R for some of them being close to 0. Our work outlined the importance of considering the effect of farm when developing Fourier-transform infrared spectroscopy prediction equations for coagulation and curd-firmness traits in goats.
受大量用于奶酪生产的山羊奶的推动，并开创山羊奶酪产业，本研究旨在通过傅里叶变换红外光谱评估农场对预测山羊乳凝乳和凝乳硬度特性的影响。从撒丁岛中部和东南部的 14 个农场的 452 只萨能奶山羊中收集了光谱。使用贝叶斯线性回归模型，同时估计所有光谱波长的影响。考虑了三个传统的乳凝特性[凝乳酶凝固时间（min），凝乳硬度达到 20mm 的时间（min）和凝乳酶添加后 30min 的凝乳硬度（mm）]和三个随时间建模的凝乳硬度测量值[根据凝乳硬度随时间变化估计的凝乳酶凝固时间（RCT），即时凝乳凝固率常数和渐近凝乳硬度]。分配了分层交叉验证（SCV），分别评估每个农场（验证集；VAL），并保留其余农场来训练（校准集）统计模型。此外，还考虑了从 VAL 农场中随机抽取的 20%的山羊（每个农场 10 个重复）进入校准集的 SCV（SCV）。为了评估模型性能，记录了决定系数（R）和验证的均方根误差。R 在 0.14 和 0.45 之间变化（即时凝乳凝固率常数和 RCT），尽管所有性状的标准差接近平均值的一半。尽管两种 SCV 程序的平均结果相似，但在 SCV 中，跨性状的 R 最大增加了约 15%，观察到的最高值为 20mm 的凝乳时间（20%），最低值为 RCT（6%）。进一步的研究表明，农场之间存在重要的可变性，其中一些农场的 R 接近 0。我们的工作概述了在开发用于山羊乳凝乳和凝乳硬度特性的傅里叶变换红外光谱预测方程时考虑农场影响的重要性。
