使用人工神经网络和响应表面方法学优化小麦造参数
Fatemeh Erfaniannejad Hosseini Nabadou1, Masoumeh Moghimi2, Aminallah Tahmasebi3
1Department of Food Science and Technology, Gonbad Kavoos Branch Islamic Azad University Gonbad Kavoos Iran.
Food science & nutrition
|May 2, 2025
概括
优化小米酒包括调整浸泡和发芽时间. 人工神经网络 (ANN) 有效地预测了麦芽质量变化,优于传统方法以获得更好的造结果.
科学领域:
- 农业科学 农业科学
- 食品科学 食品科学 食品科学
- 生物技术是生物技术.
背景情况:
- 在以谷物为基础的产品中,麦芽质量至关重要.
- 浸泡和发芽显著影响了麦芽的特性.
- 目前的监测方法昂贵且耗时.
研究的目的:
- 根据浸泡和发芽,预测小米麦芽质量的变化.
- 为了优化酒参数,以获得卓越的麦芽特性.
- 为了比较响应表面方法 (RSM) 和人工神经网络 (ANN) 的预测准确度.
主要方法:
- 响应表面方法 (RSM) 采用中央复合材料设计.
- 人工神经网络 (ANN) 建模,特别是一个前向反向传播网络 (2-6-6拓).
- 分析麦芽特性,包括麦芽效率,千粒重量,真实密度,冷水提取效率,科尔巴赫指数和提取颜色.
主要成果:
- 增加浸泡和发芽时间降低了酒效率,千粒重量和真实密度.
- 延长持续时间增加了冷水提取效率,科尔巴赫指数和提取颜色.
- 高品质麦芽的最佳条件:浸泡42.54小时,5天的发芽时间.
结论:
- 与RSM相比,ANN模型,特别是料前向反向传播网络,为小米造趋势提供了更高的预测准确性.
- 该研究确定了最佳的浸泡和发芽时间,以最大限度地提高小米麦芽质量.
- 准确预测麦芽质量属性可以简化麦芽加工过程并改善产品的一致性.
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