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使用光学传感器在线测量牛奶凝胶硬度的方法。

A method for the inline measurement of milk gel firmness using an optical sensor.

机构信息

Centre d'Innovació, Recerca i Transferència en Tecnologia dels Aliments (CIRTTA), Xarxa de referència en tecnologia dels aliments de la Generalitat de Catalunia (XaRTA), TECNIO-CERPTA, Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain; Facultad de Ingeniería Agroindustrial, Universidad de Nariño, Ciudad Universitaria Torobajo, Pasto, Nariño PC 52001, Colombia.

Centre d'Innovació, Recerca i Transferència en Tecnologia dels Aliments (CIRTTA), Xarxa de referència en tecnologia dels aliments de la Generalitat de Catalunia (XaRTA), TECNIO-CERPTA, Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain.

出版信息

J Dairy Sci. 2018 May;101(5):3910-3917. doi: 10.3168/jds.2017-13595. Epub 2018 Feb 22.

DOI:10.3168/jds.2017-13595
PMID:29477534
Abstract

At present, selection of cutting time during cheesemaking is made based on subjective methods, which has effects on product homogeneity and has prevented complete automation of cheesemaking. In this work, a new method for inline monitoring of curd firmness is presented. The method consisted of developing a model that correlates the backscatter ratio of near infrared light during milk coagulation with the rheological storage modulus. The model was developed through a factorial design with 2 factors: protein concentration (3.4 and 5.1%) and coagulation temperature (30 and 40°C). Each treatment was replicated 3 times; the model was calibrated with the first replicate and validated using the remaining 2 replicates. The coagulation process was simultaneously monitored using an optical sensor and small-amplitude oscillatory rheology. The model was calibrated and successfully validated at the different protein concentrations and coagulation temperatures studied, predicting the evolution of storage modulus during milk coagulation with coefficient of determination values >0.998 and standard error of prediction values <3.4 Pa. The results demonstrated that the proposed method allows inline monitoring of curd firming in cheesemaking and cutting the curd at a proper firmness to each type of cheese.

摘要

目前,干酪制作过程中的切割时间选择是基于主观方法,这会影响产品的均一性,并阻碍干酪制作的完全自动化。在这项工作中,提出了一种新的在线监测凝乳硬度的方法。该方法包括开发一个模型,该模型将牛奶凝固过程中近红外光的反向散射比与流变学储能模量相关联。该模型是通过具有 2 个因素的析因设计开发的:蛋白质浓度(3.4%和 5.1%)和凝固温度(30°C 和 40°C)。每种处理重复 3 次;使用第一个重复对模型进行校准,使用其余 2 个重复对其进行验证。使用光学传感器和小振幅振荡流变学同时监测凝固过程。该模型在研究的不同蛋白质浓度和凝固温度下进行了校准和成功验证,预测值的决定系数>0.998,预测值的标准误差<3.4Pa,预测值与实际值吻合良好,表明该方法可实现干酪制作过程中凝乳硬度的在线监测,并根据每种奶酪的特性将凝乳切割到适当的硬度。

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