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因果人工智能 食品质量数据的模型
1University of Zagreb Faculty of Food Technology and Biotechnology, Pierotijeva 6, 10000 Zagreb, Croatia.
Food technology and biotechnology
|April 11, 2024
概括
人工智能 (AI) 和因果建模使用大数据分析食品质量. 使用贝叶斯网络的结构因果模型 (SCM) 揭示了过程变量和消费者感知之间的因果关系,改善了食品创新.
科学领域:
- 食品科学 食品科学 食品科学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 食品产品的复杂性和高维数据阻碍了与过程变量相关的消费者感知.
- 传统的回归模型在食品数据中与混和非静止条件作斗争.
- 因果图形模型为推断食品质量分析中的因果关系提供了一个解决方案.
研究的目的:
- 强调人工智能和因果关系建模对于使用大数据进行食品质量分析的重要性.
- 用人工智能将理论知识与生产,分析和消费者评估相结合.
- 推断过程变量与消费者感官评估之间的因果关系和干预效应.
主要方法:
- 利用基于贝叶斯网络和深度学习的结构因果模型 (SCM).
- 应用LASSO规范化用于小麦烤质量数据和贝叶斯统计数据用于网络推断.
- 采用d-分离标准来阻止混效应和贝叶斯神经网络用于部分依赖图.
主要成果:
- 确定了小麦烤质量的关键预测因素,其中蛋白质含量具有最重要的直接因果关系.
- 贝叶斯网络揭示了温度,颜色和脂肪含量是发酵乳制品味道的直接原因.
- 酒精含量和挥发性酸度被确定为葡萄酒质量的关键因果因素.
结论:
- 因果AI模型,特别是贝叶斯网络SCM,有效地解决了食品质量分析中的混问题.
- 基于SCM的平均因果效应 (ACE) 推断为食品产品开发和过程改进提供了经过验证的假设.
- 这种方法支持在食品产品设计,工艺控制和营销方面做出数据驱动的决策.
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