O. Voronina, N. Kolesnik, A. Savina, R. Rykov, S. Y. Zaitsev
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引用次数: 0
摘要
相关性。山羊奶在制造功能性食品方面具有巨大潜力,包括那些具有高抗氧化活性的产品,这增加了它们的治疗和预防意义。我们提出了一个数学模型来评估山羊奶的抗氧化活性。使用 MilkoScan 7 / Fossomatic 7 DC 系统(丹麦)分析了羊奶的生化参数。水溶性抗氧化剂总量的分析是在 "Tsvetyauza 01-AA "设备上进行的。结果的统计处理在 Microsoft Excel 程序的数据分析软件包和 R 程序("Psych "软件包)中进行。采用曼-惠特尼 U 检验法评估差异的可靠性。牛奶样本取自 2022 年和 2023 年夏季的阿尔卑斯山山羊。山羊奶的抗氧化活性分别为 21.99 ± 1.48 mg/g(2022 年)和 22.30 ± 1.91 mg/g(2023 年)。所得到的最佳回归方程模型具有较高的多重相关系数(0.947),并且在 F 标准(0.01)下具有显著性。在建议的模型中,为了初步评估山羊奶的抗氧化活性,只需使用一个生化分析参数--酪蛋白。该模型可对羊奶的抗氧化活性进行初步评估。根据 t 标准,实验得出的数据与使用回归方程得出的数据之间没有明显差异。
Antioxidant activity of goat›s milk with variants of multiple regression model
Relevance. Goat’s milk has great potential for creating functional food products, including those with high antioxidant activity, which increases their therapeutic and preventive significance. A mathematical model has been proposed to evaluate the antioxidant activity of goat’s milk.Methods. The biochemical parameters of goat’s milk were analyzed using the MilkoScan 7 / Fossomatic 7 DC system (Denmark). The analysis of the total amount of water-soluble antioxidants was performed on the device “Tsvetyauza 01-AA”. Statistical processing of the results was carried out in the Microsoft Excel program using the Data Analysis package, in the R program (“Psych” package). The reliability of the differences was assessed using the Mann — Whitney U-test. Milk samples were obtained from Alpine goats in the summer of 2022 and 2023.Results. The antioxidant activity of goat›s milk was established at the level of 21.99 ± 1.48 mg/g (2022) and 22.30 ± 1.91 mg/g (2023). The best obtained regression equation model is characterized by a high multiple correlation coefficient (0.947) and is significant by the F-criterion (0.01). In the proposed model, for a preliminary assessment of the antioxidant activity of goat›s milk, it is necessary to use only one parameter of biochemical analysis — caseins. This model allows for a preliminary assessment of the antioxidant activity of goat milk. There are no significant differences between the data obtained in the experiment and the data obtained using the regression equation according to the t-criterion.