预测羊奶酪与发酵的黑果汁相结合的物理化学特性
Mohammad Mahdi Karimkhani1, Fatemeh Ebadifard2, Mohammad Ali Nematollahi3
1Department of Chemical Engineering, University of Qom, Qom 3716146611, Iran.
Food chemistry: X
|January 6, 2026
概括
机器学习预测了添加发酵黑果汁和甜菜纤维的酸奶特性. 果汁增加了抗氧化活性和协同作用,而纤维影响了粘度和保持水分的能力.
科学领域:
- 食品科学 食品科学 食品科学
- 生物技术是生物技术.
- 机器学习应用 机器学习应用
背景情况:
- 酸奶是一种受欢迎的发酵乳制品,具有重要的营养价值.
- 包括果汁和纤维等功能成分可以增强酸奶的健康益处和物理化学特性.
- 发酵的黑果汁和甜菜纤维是功能性酸奶开发的潜在成分.
研究的目的:
- 通过机器学习预测用发酵的黑果汁和甜菜纤维强化的酸奶的物理化学性质.
- 为了研究初始培养,果汁度和纤维度对酸奶特性的影响.
- 为了确定成分度和酸奶的主要特征之间的相关性.
主要方法:
- 准备的酸奶样本包括初始培养 (2种),甜菜纤维 (3种度) 和发酵黑汁 (3种度) 的变化.
- 机器学习,特别是决策树模型,被用来预测物理化学性质.
- 分析的关键性质包括pH值,外聚糖含量,协同作用,水容量,粘度和抗氧化活性.
主要成果:
- 决策树模型与观察到的物理化学性质有很高的相关性.
- 果汁和纤维度显著影响pH值,而启动型影响了外聚糖类含量.
- 合成表明与果汁度有直接关系,与纤维度有反向关系.
- 容量和粘度与果汁度相反,与纤维度直接相关.
- 抗氧化活性被黑色大果汁度强烈增强.
结论:
- 机器学习有效地预测了由功能成分影响的酸奶物理化学性质.
- 发酵的黑果汁显著提高了酸奶的抗氧化活性和协同作用.
- 甜菜纤维影响酸奶的质地,水容量和粘度.
- 优化果汁和纤维的度可以量身定制酸奶的功能和质地属性.
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