多线性和人工神经网络回归的数学优化,用于不同茶类型的矿物成分输液
Yusuf Durmus1, Ayse Dilek Atasoy2, Ahmet Ferit Atasoy3
1Department of Gastronomy and Culinary Arts, Faculty of Tourism, Artvin Çoruh University, Artvin, Turkey. yusufdurmus@artvin.edu.tr.
Scientific reports
|August 7, 2024
概括
这项研究探讨了茶的品种,度和浸泡时间如何影响矿物质含量. 确定了控制茶中的矿物质含量以获得健康益处的最佳条件.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 营养科学 营养科学
背景情况:
- 茶是全世界消费的热门饮料.
- 它的矿物质含量可能受到各种因素的影响.
- 了解这些因素对于营养价值至关重要.
研究的目的:
- 研究茶叶品种,度和浸泡时间对矿物成分的影响.
- 优化回归模型用于预测和控制矿物质含量 (Zn,K,Cu,Mg,Ca,Na,Fe,Al,Mn).
- 确定在不同类型的茶叶中实现目标矿物质度的具体条件.
主要方法:
- 对四种茶叶品种 (黑塞隆,黑土耳其,绿塞隆,绿土耳其) 的分析.
- 茶的度为1%,2%和3%,浸泡时间为2到60分钟.
- 回归分析以建模矿物质含量并优化生产参数.
主要成果:
- 矿物质含量 (Al,Cu,Fe,Mn,Na,Zn) 在不同的条件下显示出一致的变化.
- 确定了最佳浸泡条件 (1.94%度,11.4分钟),以实现土耳其黑茶的含量目标.
- 回归模型成功预测了矿物质含量,使得对所需结果的优化成为可能.
结论:
- 茶中的矿物成分受到品种,度和浸泡时间的重大影响.
- 数学优化回归方程允许精确控制茶中的矿物质含量.
- 这项研究为生产具有量身定制的营养特征的茶提供了框架.
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