Sensory profiling data consisting of judgements on a number of products with respect to a number of attributes by a number of panelists can be summarized in various ways. Besides finding components describing the main product features, there is an interest in individual panelist behavior. Earlier methods identify this by means of separate PCAs, Procrustes analyses, or three-way component methods, but these give only global comparisons of panelists. In the present paper, methods that can distinguish panelist behavior related to separate attributes, are described. These methods model the data in such a way that blocks of loadings pertaining to the attributes are either small or large. At the same time, one can zoom in on the loadings for panelists within each block of loadings associated with an attribute to inspect differences in panelist behavior. Two types of methods have been proposed for this earlier (rotation to simple blocks and penalizing blocks of loadings), and a third one is proposed in the present paper (constraining blocks of loadings to zero). The new approach is compared here to the other two methods. It is found that the rotation and constraints approaches work about equally well and better than the penalty approach. However, the rotation approach offers richer panelist behavior information, as is illustrated by the analysis of empirical data. It is also shown how, in this example, the reliability of idiosyncratic panelist behavior indicators can be evaluated.

中文翻译：

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通过旋转、约束或惩罚的块式简单组件分析，适用于产品×属性×专家组数据

可以以各种方式总结由多个小组成员对多个产品的多个属性的判断组成的感官分析数据。除了查找描述主要产品功能的组件外，还对个别小组成员的行为感兴趣。早期的方法通过单独的 PCA、Procrustes 分析或三向分量方法来识别这一点，但这些仅提供小组成员的全局比较。在本文中，描述了可以区分与不同属性相关的小组成员行为的方法。这些方法以与属性相关的加载块或小或大的方式对数据进行建模。同时，可以放大与属性相关联的每个加载块内的小组成员的加载，以检查小组成员行为的差异。早先已经为此提出了两种类型的方法（旋转到简单块和惩罚加载块），本文提出了第三种方法（将加载块限制为零）。此处将新方法与其他两种方法进行比较。发现旋转和约束方法的效果与惩罚方法一样好，甚至更好。然而，轮换方法提供了更丰富的小组成员行为信息，如经验数据分析所示。在此示例中，还展示了如何评估特殊小组成员行为指标的可靠性。此处将新方法与其他两种方法进行比较。发现旋转和约束方法的效果与惩罚方法一样好，甚至更好。然而，轮换方法提供了更丰富的小组成员行为信息，如经验数据分析所示。在此示例中，还展示了如何评估特殊小组成员行为指标的可靠性。此处将新方法与其他两种方法进行比较。发现旋转和约束方法的效果与惩罚方法一样好，甚至更好。然而，轮换方法提供了更丰富的小组成员行为信息，如经验数据分析所示。在此示例中，还展示了如何评估特殊小组成员行为指标的可靠性。