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通过使用储库计算及其灵敏度分析,从感觉的时间主导来预测时间喜好

Hiroharu Natsume¹, Shogo Okamoto¹

¹Department of Computer Science, Tokyo Metropolitan University, Hino 191-0065, Japan.

Foods (Basel, Switzerland)

|December 17, 2024

概括

这项研究使用一个水库网络来预测消费者喜欢草,基于感觉时间支配 (TDS) 数据. 该模型通过感官属性准确地估计喜欢,揭示了草偏好的关键驱动因素.

科学领域:

食品科学食品科学食品科学
感官分析感官分析
计算神经科学是一种神经科学.

背景情况:

感觉的时间支配 (TDS) 和时间喜好方法捕捉随着时间的推移动态的感觉体验和消费者偏好.
结合TDS和时间喜好数据,为在产品开发和消费者研究中分析食物口味和偏好提供了强大的框架.
了解感官属性的时间演变对于优化食品产品至关重要.

研究的目的:

通过使用先前记录的草TDS数据,从感官变化中估计时间喜欢值.
开发和验证基于感官属性的时间喜好预测模型.
引入和分析感官属性的元素和交互灵敏度对时间喜好.

主要方法:

利用储存网络,一种循环神经网络,用于分析时间序列的感觉数据.
从之前的研究中对草TDS和时间喜欢数据进行了网络训练.
计算了元素和交互灵敏度,以量化属性对喜欢的影响.

主要成果:

经过训练的水库网络模型实现了对时间喜好的高预测准确性,平均确定系数为0.951.
基本灵敏度分析确定"甜"和"水果"是草喜欢最有影响力的属性.
交互式灵敏度分析显示",甜"对喜欢有积极的影响,而"绿"和"亮"属性对喜欢有负面影响.

结论:

拟议的方法有效地将TDS结果与时间喜好联系起来,为感官数据分析提供了一种新的方法.
这种计算框架为产品评估提供了传统人体感官面板的潜在替代方案.
这些发现为感官属性和消费者对草的偏好复杂的相互作用提供了宝贵的见解.

关键词:

机器学习是机器学习.储水池计算计算的使用方法感官评估感官评估草草是一种感觉的时间主导地位.时间喜欢的喜欢.

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