Interpreting consumer preferences: physicohedonic and psychohedonic models yield different information in a coffee-flavored dairy beverage.
Interpreting consumer preferences: physicohedonic and psychohedonic models yield different information in a coffee-flavored dairy beverage.
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解释消费者的偏好：物理型和精神教育模型在咖啡味的乳制品饮料中产生不同的信息。
DOI：
10.1016/j.foodqual.2014.03.001
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发表时间：
2014-09-01
影响因子：
5.3
通讯作者：
Ziegler, Gregory R.
中科院分区：
文献类型：
--
作者：
Li, Bangde;Hayes, John E.;Ziegler, Gregory R.
关键词：
设计的实验为产品开发人员提供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。这种假设可能是无效的，特别是在感知交互发生的情况下。通过考虑喜欢强度函数可以获得更多的信息，因为单个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饮料是使用不同的咖啡提取物，液体牛奶，蔗糖和水的量的分数混合物设计配制的。388名消费者使用不完全区组设计（20个原型中的4个）评估了总体喜好（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）和厚度（厚度）的强度进行了评级。在产品平均值中，浓度变量解释了主效应多元回归中喜欢的方差的52%。蔗糖（β = 0.46）和牛奶（β = 0.46）的量对模型有显著贡献（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模型解释了平均喜欢的63%的方差;甜味（β = 0.53）和牛奶（β = 0.69）对模型有显著贡献（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和咖啡风味之间存在强线性关系，因此两个模型之间的这种差异是出乎意料的，并且可能表明添加更多的咖啡提取物也增加了负面属性，例如太多的苦味。总之，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。
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.
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影响因子：
5.3
作者：
Hough, G;Sanchez, R;Martinez, E
通讯作者：
Martinez, E
影响因子：
5.3
作者：
Ares, Gaston;Varela, Paula;Gimenez, Ana
通讯作者：
Gimenez, Ana
影响因子：
2.9
作者：
Hayes, John E.;Sullivan, Bridget S.;Duffy, Valerie B.
通讯作者：
Duffy, Valerie B.
影响因子：
3.5
作者：
Hayes, John E.;Duffy, Valerie B.
通讯作者：
Duffy, Valerie B.
DOI：
10.1037/h0077582
发表时间：
1979-01-01
期刊：
JOURNAL OF COMPARATIVE AND PHYSIOLOGICAL PSYCHOLOGY
影响因子：
--
作者：
LAWLESS, HT
通讯作者：
LAWLESS, HT