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使用多元线性回归(MLR)并结合流变学、物理和化学数据估算塞尔帕奶酪的成熟时间。

Estimation of Serpa cheese ripening time using multiple linear regression (MLR) considering rheological, physical and chemical data.

作者信息

Alvarenga Nuno, Silva Paula, Garcia José Rodriguez, Sousa Isabel

机构信息

Instituto Politécnico de Beja - Escola Superior Agrária, Rua Pedro Soares, Apartado 6158, 7801-908 Beja, Portugal.

出版信息

J Dairy Res. 2008 May;75(2):233-9. doi: 10.1017/S0022029908003191.

Abstract

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 R2=0.98, P<0.0001).

摘要

生羊奶半软质奶酪(RESS奶酪)在葡萄牙和欧洲其他几个地区都是重要的产品。奶油般的质地是这些奶酪的一个基本属性,它源于一些尚未完全明确的结构特性。在此,通过小振幅振荡剪切(SAOS)分析了一种名为塞尔帕奶酪的传统RESS奶酪在成熟期间发生的结构变化。流变学数据辅以成熟过程中监测的其他物理和化学参数,以便使用多元线性回归(MLR)来估计塞尔帕奶酪的成熟时间。力学谱表明,在成熟初期结构相对较强,类似于凝胶,对频率的依赖性较低;而在成熟末期,结构较弱,类似于浓悬浮液,在图表左侧有一个交叉点(G'' = G'),且两个模量都高度依赖于频率。结构(硬度和储能模量)与蛋白水解指标之间具有良好的相关性(P < 0.05)。通过结合化学、颜色和流变学参数,我们得到了一个多元线性回归(MLR)模型,该模型能够估计塞尔帕奶酪的成熟时间,估计误差为1.7天(调整后的R2 = 0.98,P < 0.0001)。

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