Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.
Experimental Zooprophylactic Institute Lazio and Toscana "Mariano Aleandri", Via Appia Nuova 1411, 00178 Rome, Italy.
J Dairy Sci. 2017 Sep;100(9):7083-7087. doi: 10.3168/jds.2017-12707. Epub 2017 Jun 28.
Milk coagulation and acidity traits are important factors to inform the cheesemaking process. Those traits have been deeply studied in bovine milk, whereas scarce information is available for buffalo milk. However, the dairy industry is interested in a method to determine milk coagulation and acidity features quickly and in a cost-effective manner, which could be provided by Fourier-transform mid-infrared (FT-MIR) spectroscopy. The aim of this study was to evaluate the potential of FT-MIR to predict coagulation and acidity traits of Mediterranean buffalo milk. A total of 654 records from 36 herds located in central Italy with information on milk yield, somatic cell score, milk chemical composition, milk acidity [pH, titratable acidity (TA)], and milk coagulation properties (rennet coagulation time, curd firming time, and curd firmness) were available for statistical analysis. Reference measures of milk acidity and coagulation properties were matched with milk spectral information, and FT-MIR prediction models were built using partial least squares regression. The data set was divided into a calibration set (75%) and a validation set (25%). The capacity of FT-MIR spectroscopy to correctly classify milk samples based on their renneting ability was evaluated by a canonical discriminant analysis. Average values for milk coagulation traits were 13.32 min, 3.24 min, and 39.27 mm for rennet coagulation time, curd firming time, and curd firmness, respectively. Milk acidity traits averaged 6.66 (pH) and 7.22 Soxhlet-Henkel degrees/100 mL (TA). All milk coagulation and acidity traits, except for pH, had high variability (17 to 46%). Prediction models of coagulation traits were moderately to scarcely accurate, whereas the coefficients of determination of external validation were 0.76 and 0.66 for pH and TA, respectively. Canonical discriminant analysis indicated that information on milk coagulating ability is present in the MIR spectra, and the model correctly classified as noncoagulating the 91.57 and 67.86% of milk samples in the calibration and validation sets, respectively. In conclusion, our results can be relevant to the dairy industry to classify buffalo milk samples before processing.
乳凝和酸度特性是告知干酪制作过程的重要因素。这些特性在牛乳中得到了深入研究，而关于水牛乳的信息则很少。然而，乳制品行业感兴趣的是一种能够快速且具有成本效益地确定乳凝和酸度特征的方法，这可以通过傅里叶变换中红外（FT-MIR）光谱来实现。本研究的目的是评估 FT-MIR 预测地中海水牛乳乳凝和酸度特性的潜力。共有 36 个意大利中部奶农的 654 个样本记录，其中包括产奶量、体细胞评分、乳化学成分、乳酸度（pH、滴定酸度（TA））和乳凝特性（凝乳酶凝固时间、凝乳凝固时间和凝乳硬度）。参考乳酸度和凝乳特性的测量值与乳光谱信息相匹配，使用偏最小二乘回归建立 FT-MIR 预测模型。数据集分为校准集（75%）和验证集（25%）。使用典型判别分析评估 FT-MIR 光谱根据其凝乳能力正确分类乳样的能力。乳凝特性的平均值分别为凝乳酶凝固时间 13.32 分钟、凝乳凝固时间 3.24 分钟和凝乳硬度 39.27mm。乳酸度特性的平均值分别为 6.66（pH）和 7.22 索氏-亨克尔度/100mL（TA）。除了 pH 值外，所有乳凝和酸度特性的变异性都很高（17%至 46%）。凝乳特性的预测模型的准确性中等至较低，而 pH 和 TA 的外部验证的决定系数分别为 0.76 和 0.66。典型判别分析表明，乳凝能力的信息存在于 MIR 光谱中，模型分别正确地将校准集和验证集中 91.57%和 67.86%的非凝乳样品分类。总之，我们的结果可能与乳制品行业相关，可以在加工前对水牛乳样品进行分类。
