Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.
Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.
J Dairy Sci. 2019 Mar;102(3):1927-1932. doi: 10.3168/jds.2018-15259. Epub 2019 Jan 3.
Sheep milk is mainly transformed into cheese; thus, the dairy industry seeks more rapid and cost-effective methods of analysis to determine milk coagulation and acidity traits. This study aimed to assess the feasibility of Fourier-transform mid-infrared spectroscopy to determine milk coagulation and acidity traits of sheep bulk milk and to classify milk samples according to their renneting capacity. A total of 465 bulk milk samples collected in 140 single-breed flocks of Comisana (84 samples, 24 flocks) and Sarda (381 samples, 116 flocks) breeds located in Central Italy were analyzed for coagulation properties (rennet coagulation time, curd firming time, and curd firmness) and acidity traits (pH and titratable acidity) using standard laboratory procedures. Fourier-transform mid-infrared spectroscopy prediction models for these traits were built using partial least squares regression analysis and were externally validated by randomly dividing the full data set into a calibration set (75%) and a validation set (25%). The discriminant capacity of the rennet coagulation time prediction model was determined using partial least squares discriminant analysis. Prediction models were more accurate for acidity traits than for milk coagulation properties, and the ratio of prediction to deviation ranged from 1.01 (curd firmness) to 2.14 (pH). Moreover, the discriminant analysis led to an overall accuracy of 74 and 66% for the calibration and validation sets, respectively, with greater sensitivity for samples that coagulated between 10 and 20 min and greater specificity to detect early-coagulating (<10 min) and late-coagulating (20-30 min) samples. Results suggest that Fourier-transform mid-infrared spectroscopy has the potential to help the dairy sheep industry identify milk with better coagulation ability for cheese production and thus improve milk transformation efficiency. However, further research is needed before this information can be exploited at the industry level.
绵羊奶主要转化为奶酪；因此，乳制品行业寻求更快速和更具成本效益的分析方法来确定牛奶的凝结和酸度特性。本研究旨在评估傅里叶变换中红外光谱法用于测定绵羊混合牛奶凝结和酸度特性的可行性，并根据其凝乳能力对牛奶样品进行分类。共分析了来自意大利中部的 140 个科马萨纳（84 个样本，24 个羊群）和萨达（381 个样本，116 个羊群）品种的 465 个批量牛奶样本，用于凝结特性（凝乳酶凝结时间、凝块凝固时间和凝块硬度）和酸度特性（pH 和滴定酸度），使用标准实验室程序。使用偏最小二乘回归分析建立了这些特性的傅里叶变换中红外光谱预测模型，并通过随机将完整数据集分为校准集（75%）和验证集（25%）来进行外部验证。使用偏最小二乘判别分析确定了凝乳酶凝结时间预测模型的判别能力。与牛奶凝结特性相比，预测模型对酸度特性更准确，预测与偏差的比例范围从 1.01（凝块硬度）到 2.14（pH）。此外，判别分析导致校准集和验证集的总体准确率分别为 74%和 66%，对凝结时间在 10 到 20 分钟之间的样品具有更高的灵敏度，对早期凝结（<10 分钟）和晚期凝结（20-30 分钟）的样品具有更高的特异性。结果表明，傅里叶变换中红外光谱法有可能帮助奶绵羊产业识别出更适合奶酪生产的凝结能力更好的牛奶，从而提高牛奶转化效率。然而，在该信息可以在行业层面得到利用之前，还需要进一步的研究。
