Maximizing overall liking results in a superior product to minimizing deviations from ideal ratings: an optimization case study with coffee-flavored milk.
Maximizing overall liking results in a superior product to minimizing deviations from ideal ratings: an optimization case study with coffee-flavored milk.
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DOI：
10.1016/j.foodqual.2015.01.011
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发表时间：
2015-06-01
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.
在几乎正确（just-about-right，缩写为EAD）缩放和理想缩放中，属性delta（即，“太少”或“太多”）反映了受试者对相对于其假设理想的属性的不满意程度。不满意（属性delta）是一个不同于消费者可接受性的结构，可操作为喜欢。因此，我们假设最小化不满和最大化喜欢会产生不同的最佳配方。这项研究的目的是比较产品优化策略，即最大化的喜欢维斯最小化的不满。咖啡风味的乳制品饮料（n = 20）的配制使用的部分混合物的设计，限制咖啡提取物，牛奶，蔗糖和水的比例。参与者（n = 388）被随机分配到三个研究条件之一，他们使用不完全区组设计评估20个样本中的4个。对样品的总体喜好和属性甜度、奶味、稠度和咖啡味的强度进行评级。在适当情况下，将总体产品质量的测量值（Ideal_Delta和Ideal_Delta）计算为四个属性Delta的绝对值之和。通过以下方式估计最佳制剂：a）最大化喜好; B）最小化Ideal_Delta;或c）最小化Ideal_Delta。进行了验证研究，以评价产品优化模型。与通过最大化喜欢获得的配方相比，参与者在不满意模型中表示对咖啡味乳制品饮料的偏好，该咖啡味乳制品饮料具有更多的咖啡提取物和较少的牛奶和蔗糖。也就是说，当喜好被优化时，参与者通常喜欢更淡，更奶，更甜的咖啡味乳制品饮料。预测的喜欢分数在随后的实验中进行了验证，并制定最佳产品，以最大限度地喜欢显着优于制定，以最大限度地减少不满意的配对偏好测试。这些发现与以下观点一致：当评价喜欢时，评价方法和理想缩放方法都存在不存在的态度偏差。也就是说，消费者真诚地认为他们想要“黑暗，丰富，丰盛”的咖啡，而事实并非如此。本文还展示了精益实验方法的实用性和效率。
In just-about-right (JAR) scaling and ideal scaling, attribute delta (i.e., “Too Little” or “Too Much”) reflects a subject’s dissatisfaction level for an attribute relative to their hypothetical ideal. Dissatisfaction (attribute delta) is a different construct from consumer acceptability, operationalized as liking. Therefore, we hypothesized minimizing dissatisfaction and maximizing liking would yield different optimal formulations. The objective of this research was to compare product optimization strategies, i.e. maximizing liking vis-à-vis minimizing dissatisfaction. Coffee-flavored dairy beverages (n = 20) were formulated using a fractional mixture design that constrained the proportions of coffee extract, milk, sucrose, and water. Participants (n = 388) were randomly assigned to one of three research conditions, where they evaluated 4 of the 20 samples using an incomplete block design. Samples were rated for overall liking and for intensity of the attributes sweetness, milk flavor, thickness and coffee flavor. Where appropriate, measures of overall product quality (Ideal_Delta and JAR_Delta) were calculated as the sum of the absolute values of the four attribute deltas. Optimal formulations were estimated by: a) maximizing liking; b) minimizing Ideal_Delta; or c) minimizing JAR_Delta. A validation study was conducted to evaluate product optimization models. Participants indicated a preference for a coffee-flavored dairy beverage with more coffee extract and less milk and sucrose in the dissatisfaction model compared to the formula obtained by maximizing liking. That is, when liking was optimized, participants generally liked a weaker, milkier and sweeter coffee-flavored dairy beverage. Predicted liking scores were validated in a subsequent experiment, and the optimal product formulated to maximize liking was significantly preferred to that formulated to minimize dissatisfaction by a paired preference test. These findings are consistent with the view that JAR and ideal scaling methods both suffer from attitudinal biases that are not present when liking is assessed. That is, consumers sincerely believe they want ‘dark, rich, hearty’ coffee when they do not. This paper also demonstrates the utility and efficiency of a lean experimental approach.
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影响因子：
7.1
作者：
DREWNOWSKI, A;MOSKOWITZ, HR
通讯作者：
MOSKOWITZ, HR
影响因子：
5.3
作者：
Harwood, Meriel L.;Ziegler, Gregory R.;Hayes, John E.
通讯作者：
Hayes, John E.
影响因子：
2
作者：
Gilbert, JM;Young, H;Murray, SH
通讯作者：
Murray, SH
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
作者：
Bower, JA;Boyd, R
通讯作者：
Boyd, R