感觉偏差的自编码器可以从食品类风湿学中预测纹理感知
Paul M Kraessig1, Shyamvanshikumar P Singh1, Jiakai Lu2
1Transport Phenomena Laboratory, Department of Food Science, Purdue University, West Lafayette, IN, USA.
Food research international (Ottawa, Ont.)
|March 3, 2025
概括
这项研究使用机器学习将液体食品质感与人们如何感知其联系起来. 这些发现有助于创造具有特定感官体验的食物,改善食品产品的开发.
科学领域:
- 食品科学 食品科学 食品科学
- 感官科学 感官科学
- 机器学习 机器学习
背景情况:
- 了解食物的物理特性与感官感知之间的联系对于食品设计至关重要.
- 非牛顿式的风湿学属性显著影响液体食品的质地.
- 现有的方法难以捕捉感官数据中的复杂,非线性关系.
研究的目的:
- 开发一种机器学习策略,来解码非牛顿式的质性质和液体食品中感知到的纹理之间的关系.
- 为了确定剪切稀释特性和感知厚度之间的非线性,非注射关系.
- 为推进食品产品开发和感官设计提供一种新的方法.
主要方法:
- 使用自编码神经网络实施了一种创新的机器学习策略.
- 在自动编码器的训练阶段,作为解码器偏差的内置感官得分.
- 分析了液体食品的非牛顿式风湿学属性,并将它们与感知质感相关联.
主要成果:
- 成功确定了剪切稀释特性和感知厚度之间的复杂,非线性关系.
- 证明了自动编码器在解码感官感知中的有效性,即使使用小数据集.
- 验证了基于rheological数据预测感知质感的能力.
结论:
- 拟议的机器学习策略有效地解码了质性质和感知质感之间的联系.
- 这种方法有助于设计具有量身定制感官特征的液体食品.
- 为食品产品开发和感官工程领域的创新提供了一个有前途的工具.
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