Stocco G, Cipolat-Gotet C, Bonfatti V, Schiavon S, Bittante G, Cecchinato A
Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, viale dell'Università 16 - 35020 Legnaro (PD), Italy.
Department of Comparative Biomedicine and Food Science (BCA), University of Padova, viale dell'Università 16 - 35020 Legnaro (PD), Italy.
J Dairy Sci. 2016 Nov;99(11):8680-8686. doi: 10.3168/jds.2016-11303. Epub 2016 Sep 7.
The aims of this study were (1) to assess variability in the major mineral components of buffalo milk, (2) to estimate the effect of certain environmental sources of variation on the major minerals during lactation, and (3) to investigate the possibility of using Fourier-transform infrared (FTIR) spectroscopy as an indirect, noninvasive tool for routine prediction of the mineral content of buffalo milk. A total of 173 buffaloes reared in 5 herds were sampled once during the morning milking. Milk samples were analyzed for Ca, P, K, and Mg contents within 3h of sample collection using inductively coupled plasma optical emission spectrometry. A Milkoscan FT2 (Foss, Hillerød, Denmark) was used to acquire milk spectra over the spectral range from 5,000 to 900 wavenumber/cm. Prediction models were built using a partial least square approach, and cross-validation was used to assess the prediction accuracy of FTIR. Prediction models were validated using a 4-fold random cross-validation, thus dividing the calibration-test set in 4 folds, using one of them to check the results (prediction models) and the remaining 3 to develop the calibration models. Buffalo milk minerals averaged 162, 117, 86, and 14.4mg/dL of milk for Ca, P, K, and Mg, respectively. Herd and days in milk were the most important sources of variation in the traits investigated. Parity slightly affected only Ca content. Coefficients of determination of cross-validation between the FTIR-predicted and the measured values were 0.71, 0.70, and 0.72 for Ca, Mg, and P, respectively, whereas prediction accuracy was lower for K (0.55). Our findings reveal FTIR to be an unsuitable tool when milk mineral content needs to be predicted with high accuracy. Predictions may play a role as indicator traits in selective breeding (if the additive genetic correlation between FTIR predictions and measures of milk minerals is high enough) or in monitoring the milk of buffalo populations for dairy industry purposes.
（1）评估水牛奶中主要矿物质成分的变异性；（2）估计泌乳期间某些环境变异源对主要矿物质的影响；（3）研究使用傅里叶变换红外（FTIR）光谱作为间接、非侵入性工具对水牛奶矿物质含量进行常规预测的可能性。对5个牛群中饲养的173头水牛在早晨挤奶时进行了一次采样。采集的牛奶样本在3小时内使用电感耦合等离子体发射光谱法分析钙、磷、钾和镁的含量。使用Milkoscan FT2（丹麦希勒勒德福斯公司）在5000至900波数/厘米的光谱范围内采集牛奶光谱。采用偏最小二乘法建立预测模型，并使用交叉验证来评估FTIR的预测准确性。使用4倍随机交叉验证对预测模型进行验证，即将校准测试集分为4份，用其中一份检查结果（预测模型），其余3份用于建立校准模型。水牛奶中钙、磷、钾和镁的矿物质平均含量分别为每分升牛奶162毫克、117毫克、86毫克和14.4毫克。牛群和泌乳天数是所研究性状变异的最重要来源。胎次仅对钙含量有轻微影响。FTIR预测值与测量值之间的交叉验证决定系数，钙、镁和磷分别为0.71、0.70和0.72，而钾的值较低（0.55）。我们的研究结果表明，当需要高精度预测牛奶矿物质含量时，FTIR是不合适的工具。预测在选择性育种中（如果FTIR预测与牛奶矿物质测量值之间的加性遗传相关性足够高）或在为乳制品行业目的监测水牛群体的牛奶方面可能作为指示性状发挥作用。
