Li Bangde, Hayes John E, Ziegler Gregory R
Sensory Evaluation Center, College of Agricultural Sciences, The Pennsylvania State University, University Park, PA, USA ; Department of Food Science, College of Agricultural Sciences, The Pennsylvania State University, University Park, PA, USA.
Department of Food Science, College of Agricultural Sciences, The Pennsylvania State University, University Park, PA, USA.
Food Qual Prefer. 2015 Jun 1;42:27-36. doi: 10.1016/j.foodqual.2015.01.011.
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 and for intensity of the attributes and . Where appropriate, measures of overall product quality ( and ) were calculated as the sum of the absolute values of the four attribute deltas. Optimal formulations were estimated by: a) maximizing ; b) minimizing ; or c) minimizing . 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 . That is, when was optimized, participants generally liked a weaker, milkier and sweeter coffee-flavored dairy beverage. Predicted scores were validated in a subsequent experiment, and the optimal product formulated to maximize 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.
在恰如其分（JAR）标度法和理想标度法中，属性差值（即“太少”或“太多”）反映了受试者对某一属性相对于其假设理想状态的不满意程度。不满意程度（属性差值）与消费者可接受性是不同的概念，消费者可接受性以喜好程度来衡量。因此，我们假设将不满意程度降至最低并将喜好程度最大化会产生不同的最优配方。本研究的目的是比较产品优化策略，即最大化喜好程度与最小化不满意程度。采用分数混合设计来调配咖啡风味乳饮料（n = 20），该设计限定了咖啡提取物、牛奶、蔗糖和水的比例。参与者（n = 388）被随机分配到三种研究条件之一，在这些条件下，他们使用不完全区组设计对20个样品中的4个进行评估。对样品的整体情况以及属性 和 的强度进行评分。在适当的情况下，整体产品质量（ 和 ）的度量值计算为四个属性差值绝对值的总和。通过以下方式估计最优配方：a）最大化 ；b）最小化 ；或c）最小化 。进行了一项验证研究以评估产品优化模型。与通过最大化 得到的配方相比，参与者表示在不满意模型中更喜欢咖啡提取物含量更高、牛奶和蔗糖含量更低的咖啡风味乳饮料。也就是说，当 得到优化时，参与者通常更喜欢味道较淡、奶味更浓且更甜的咖啡风味乳饮料。预测的 分数在后续实验中得到验证，通过配对偏好测试，与为最小化不满意程度而配制的产品相比，为最大化 而配制的最优产品更受青睐。这些发现与以下观点一致，即JAR和理想标度法都存在态度偏差，而在评估喜好程度时不存在这种偏差。也就是说，消费者真心认为他们想要“深色、浓郁、丰盛”的咖啡，但实际上并非如此。本文还展示了精简实验方法的实用性和效率。
