Biegalski Jakub, Cais-Sokolińska Dorota, Wawrzyniak Jolanta
Department of Dairy and Process Engineering, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, ul. Wojska Polskiego 31, 60-624 Poznan, Poland.
Foods. 2022 Jan 22;11(3):296. doi: 10.3390/foods11030296.
The aim of the present study was to analyze the impact of cheese fragmentation and packaging on the dynamics of water-fat serum released from pasta filata cheese made from cow's milk and its mixture with sheep's milk. The addition of sheep's milk reduced the amount of leachate from the vacuum-packed cheeses and did not cause as much loss of gloss as in the case of cow's milk cheeses. This was also reflected in the microscopic images of the cheese samples. Consumers showed less acceptance of cow's milk pasta filata cheeses than cheeses made with a mixture of cow's and sheep's milk (they had the same fat content, acidity, hardness, and oiling-off, but better stretching). The data describing water-fat serum release from pasta filata cheese within 24 h of unpacking was modeled with the use of the feed-forward artificial neural networks, whose architecture is based on Multi-Layer Perceptron with a single hidden layer. The model inputs comprised four independent variables, including one quantitative (i.e., time) and the other qualitative ones, which had the following states: type of raw material (cow's milk, cow-sheep's milk), way of sample portioning (whole, quarters, slices), packing method (vacuum packed and packed in brine).
本研究的目的是分析奶酪破碎和包装对由牛奶及其与羊奶混合制成的丝状干酪释放水脂肪血清动力学的影响。添加羊奶减少了真空包装奶酪的渗滤液量，且不像牛奶奶酪那样导致大量光泽损失。这也反映在奶酪样品的微观图像中。与用牛奶和羊奶混合制成的奶酪相比，消费者对牛奶丝状干酪的接受度较低（它们具有相同的脂肪含量、酸度、硬度和析油情况，但拉伸性更好）。使用前馈人工神经网络对描述丝状干酪在 unpacking 后 24 小时内水脂肪血清释放的数据进行建模，该神经网络的架构基于具有单个隐藏层的多层感知器。模型输入包括四个自变量，其中一个是定量的（即时间），其他是定性的，具有以下状态：原料类型（牛奶、牛奶 - 羊奶）、样品分割方式（整块、四分之一块、切片）、包装方法（真空包装和盐水包装）。
