用组合脱水桃子增强饼干配方:用于技术质量评估和优化的机器学习方法
Biljana Lončar1, Lato Pezo2, Violeta Knežević1
1Faculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia.
Foods (Basel, Switzerland)
|March 13, 2024
概括
人工神经网络 (ANN) 和支持矢量机器 (SVM) 模型准确地预测了脱水桃子的饼干质量. 最佳的饼干配方包括15%的透前处理的冷解桃子,以提高质量.
科学领域:
- 食品科学 食品科学 食品科学
- 机器学习应用 机器学习应用
- 营养分析 营养分析
背景情况:
- 饼干质量受到成分和加工方法的影响.
- 脱水的水果可以增强制品的营养和感官特性.
- 预测模型有助于优化食品产品开发.
研究的目的:
- 使用人工神经网络 (ANN) 和支持矢量机器 (SVM) 模型预测和优化cookie质量参数.
- 调查脱水桃子和预处理方法对饼干属性的影响.
- 确定最佳配方,以提高饼干质量.
主要方法:
- 为32个不同的cookie质量参数开发单独的ANN和SVM模型.
- 敏感性分析的应用,以确定关键的影响因素.
- 使用ANN模型进行多目标优化,以找到理想的配方参数.
主要成果:
- 对于各种质量参数,ANN模型实现了高预测准确性 (r2高达1000).
- 对于特定变量,SVM模型表现强 (r2高达0.981).
- 脱水的桃子和透预处理显著影响了饼干的组成和质量.
结论:
- ANN和SVM是预测和优化果实丰富饼干质量的有效工具.
- 透式预处理增强了饼干配方中的冷解桃子的好处.
- 鉴于优越的饼干质量,确定了15%的透性预处理冷解粉化桃子的最佳配方.
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