Designed experiments provide product developers feedback on the relationship between formulation and consumer acceptability. While actionable, this approach typically assumes a simple psychophysical relationship between ingredient concentration and perceived intensity. This assumption may not be valid, especially in cases where perceptual interactions occur. Additional information can be gained by considering the liking-intensity function, as single ingredients can influence more than one perceptual attribute. Here, 20 coffee-flavored dairy beverages were formulated using a fractional mixture design that varied the amount of coffee extract, fluid milk, sucrose, and water. Overall liking (liking) was assessed by 388 consumers using an incomplete block design (4 out of 20 prototypes) to limit fatigue; all participants also rated the samples for intensity of coffee flavor (coffee), milk flavor (milk), sweetness (sweetness) and thickness (thickness). Across product means, the concentration variables explained 52% of the variance in liking in main effects multiple regression. The amount of sucrose (β = 0.46) and milk (β = 0.46) contributed significantly to the model (p's <0.02) while coffee extract (β = -0.17; p = 0.35) did not. A comparable model based on the perceived intensity explained 63% of the variance in mean liking; sweetness (β = 0.53) and milk (β = 0.69) contributed significantly to the model (p's <0.04), while the influence of coffee flavor (β = 0.48) was positive but marginally (p = 0.09). Since a strong linear relationship existed between coffee extract concentration and coffee flavor, this discrepancy between the two models was unexpected, and probably indicates that adding more coffee extract also adds a negative attribute, e.g. too much bitterness. In summary, modeling liking as a function of both perceived intensity and physical concentration provides a richer interpretation of consumer data.

中文翻译：

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解读消费者偏好：生理享乐和心理享乐模型在咖啡味乳品饮料中产生不同的信息

设计的实验为产品开发人员提供了有关配方和消费者可接受性之间关系的反馈。虽然可行，但这种方法通常假设成分浓度和感知强度之间存在简单的心理物理关系。这种假设可能无效，尤其是在发生感知交互的情况下。通过考虑喜好强度函数可以获得额外的信息，因为单一成分可以影响多个感知属性。在这里，20 种咖啡风味的乳制品使用不同的咖啡提取物、液态奶、蔗糖和水的量的分数混合设计配制而成。388 名消费者使用不完整的块设计（20 个原型中的 4 个）来评估整体喜好（喜好）以限制疲劳；所有参与者还对样品的咖啡风味（咖啡）、牛奶风味（牛奶）、甜度（甜度）和稠度（厚度）进行了评级。在产品均值中，浓度变量解释了主效应多元回归中 52% 的喜好差异。蔗糖 (β = 0.46) 和牛奶 (β = 0.46) 的量对模型有显着影响 (p < 0.02)，而咖啡提取物 (β = -0.17；p = 0.35) 则没有。基于感知强度的可比较模型解释了平均喜好差异的 63%；甜度 (β = 0.53) 和牛奶 (β = 0.69) 对模型有显着影响 (p <0.04)，而咖啡风味 (β = 0.48) 的影响是积极的，但微不足道 (p = 0.09)。由于咖啡提取物浓度与咖啡风味之间存在很强的线性关系，两种模型之间的这种差异是出乎意料的，可能表明添加更多的咖啡提取物也会带来负面影响，例如苦味过多。总而言之，将喜好建模为感知强度和身体集中度的函数提供了对消费者数据的更丰富的解释。