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首页> 外文期刊>Journal of dairy research >Estimation of Serpa cheese ripening time using multiple linear regression (MLR) considering rheological, physical and chemical data
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Estimation of Serpa cheese ripening time using multiple linear regression (MLR) considering rheological, physical and chemical data

机译：考虑流变，物理和化学数据的多元线性回归（MLR）估算Serpa奶酪的成熟时间

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摘要

Raw ewes' milk semi-soft cheeses (RESS-cheeses) are important products in Portugal and in several European regions. Creamy texture is an essential attribute of these cheeses, which results from structural properties that are not always well characterized. Here, the structural changes occurring during the ripening period of a traditional RESS-cheese, known as Serpa cheese, were analysed through small amplitude oscillatory shear (SAOS). Rheological data was complemented with other physical and chemical parameters, that were monitored during ripening, in order to estimate Serpa cheese ripening time using multiple linear regression (MLR). Mechanical spectra indicated a relatively strong structure, comparable to a gel, with a low dependence on frequency at the beginning of ripening and a weak structure, comparable to a concentrated suspension, with a crossing point (G"=G') at the left of the graphic and with both moduli highly dependent on frequency, at the end of ripening. Good correlations (P<0·05) were obtained between structural (hardness and storage modulus) and proteolysis indicators. Using a combination of chemical, colour and rheological parameters we were able to obtain a multiple linear regression (MLR) which allows the estimation of Serpa cheese ripening time with an estimation error of 1·7 d (adjusted R~2 = 0·98, P<0·0001).
机译：生母羊的牛奶半软奶酪（RESS-奶酪）在葡萄牙和一些欧洲地区是重要产品。乳脂质地是这些奶酪的基本属性，这是由于结构特性无法始终很好地表征而导致的。在这里，通过小振幅振荡剪切（SAOS）分析了传统RESS奶酪（称为Serpa奶酪）成熟期间发生的结构变化。流变学数据与其他物理和化学参数互为补充，这些参数在成熟过程中进行监控，以便使用多元线性回归（MLR）估算Serpa奶酪的成熟时间。机械光谱表明，与凝胶相当的相对较强的结构，在成熟开始时对频率的依赖性低，而与浓缩的悬浮液相当的较弱的结构，在交叉点的左侧具有交叉点（G“ = G'）。在成熟时，图形和两个模量都高度依赖于频率，在结构（硬度和储能模量）与蛋白水解指标之间获得了良好的相关性（P <0·05），并结合使用了化学，颜色和流变学参数我们能够获得多元线性回归（MLR），该线性回归可以估算Serpa奶酪的成熟时间，且估算误差为1·7 d（调整后R〜2 = 0·98，P <0·0001）。

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