一种机器学习方法,调查消费者对刚刚可见的颜色差异和固化的颜色特征表格尺度的熟悉度和参与度
Guillermo Ripoll1,2, Begoña Panea1,2, María Ángeles Latorre2,3
1Animal Science Department, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Avda. Montañana 930, 50059 Zaragoza, Spain.
Foods (Basel, Switzerland)
|December 23, 2023
概括
了解干燥固化火腿的颜色是关键. 这项研究发现,消费者参与和年龄影响色彩感知,建立了6.2.2的Just Noticeable Color Difference (JNCD). 机器学习改善了色彩分析.
科学领域:
- 食品科学 食品科学 食品科学
- 感官科学 感官科学
- 测色仪的颜色测量方法
背景情况:
- 视觉色彩感知对于食品质量评估至关重要.
- 仪器颜色测量提供客观数据,但需要与人类感知相关的解释.
- 消费者对食物颜色的看法受到各种因素的影响,包括熟悉和参与.
研究的目的:
- 为了确定仪器颜色和干燥固化火腿的视觉色彩感知之间的关系.
- 为了确定干燥固化火腿颜色的可观察到的颜色差异 (JNCD).
- 调查消费者参与和熟悉对色彩感知和JNCD的影响.
主要方法:
- 干燥固化的火腿切片的仪器色彩分析.
- 消费者调查评估颜色评分,匹配,参与和熟悉度.
- 根据参与程度对消费者进行集群.
- 计算不同消费者集群的JNCD.
- 可解释机器学习的应用,以关联视觉和仪器色彩数据.
主要成果:
- 对于干燥固化的火腿颜色,确定了 ΔEab* = 6.2 的 JNCD.
- 发现JNCD对于年轻消费者来说较低.
- 消费者参与显著影响了色彩感知和JNCD.
- 结合心理数据的机器学习模型的表现优于多重线性回归.
- 在机器学习模型中,L*和hab被确定为最有影响力的颜色变量.
结论:
- 消费者的参与和熟悉性显著影响了对干燥固化火腿颜色的看法.
- 客观色彩测量 (L*,hab) 与机器学习相结合,可以有效地模拟视觉色彩感知.
- 已建立的JNCD为干燥硬化火腿行业的质量控制和产品开发提供了一个基准.
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