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The application of artificial neural networks (ANN) for the denaturation of meat proteins – the kinetic analysis method

机译:人工神经网络(ANN)在肉蛋白质变性中的应用-动力学分析方法

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Background. Artificial neural networks (ANN) are a common mathematical tool widely used in many research fields. Since they are applicable to non-linear relationships and do not require preliminary assumptions, they are a particularly promising tool in relation to meat processing. Thermal denaturation contains??? a lot of information concerning the quality of meats. The aim was to create a methodology of kinetic analysis to obtain a quick and accurate tool for meat protein denaturation in non-isothermal conditions based on?? The Coats-Redfern equation with the use of ANN. Materials and methods. The analyses were carried out on samples of minced samples of Longissimus dorsi (pork). Thermal properties were determined using the differential scanning calorimetry (DSC) method with a Q100 TA Instruments apparatus. The data obtained was processed using the artificial neural network module in Statistica 13.0 software. Results. The following models fit well with experimental data: F1 and F2 ( r = 0.99, F Snedecor’s F statistics 836943.20 and 971947.41 respectively). Deviations from experimental conversion degrees were higher for model F2, while for F1, good conformity was obtained across the whole range of α( T ). Conclusions. This preliminary study confirmed that methods of traditional kinetics of processes in non-isothermal conditions based on the Coats-Redfern equation can be successfully applied to meat protein denaturation. The method of kinetic analysis allows a high level of accuracy to be achieved and meets the requirements of an efficient engineering tool.
机译:背景。人工神经网络(ANN)是广泛用于许多研究领域的通用数学工具。由于它们适用于非线性关系并且不需要初步假设,因此它们在肉类加工中是特别有前途的工具。热变性包含???有关肉质的很多信息。目的是建立一种动力学分析的方法,以在非等温条件下获得快速准确的肉蛋白质变性工具。使用ANN的Coats-Redfern方程。材料和方法。分析是对长背材(猪肉)切碎的样品进行的。使用差示扫描量热法(DSC)和Q100 TA Instruments仪器确定热性能。使用Statistica 13.0软件中的人工神经网络模块处理获得的数据。结果。以下模型与实验数据非常吻合:F1和F2(r = 0.99,F Snedecor的F统计量分别为836943.20和971947.41)。对于模型F2,与实验转换程度的偏差更大,而对于F1,在整个α(T)范围内都获得了良好的一致性。结论。这项初步研究证实,基于Coats-Redfern方程的非等温条件下传统工艺动力学方法可以成功地应用于肉蛋白质变性。动力学分析方法可以实现很高的准确性,并满足高效工程工具的要求。

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