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CLV3W: A clustering around latent variables approach to detect panel disagreement in three-way conventional sensory profiling data

机译：CLV3W：围绕潜在变量的聚类方法，用于检测三向常规感官分析数据中的面板差异

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摘要

To detect panel disagreement, we propose the clustering around latent variables for three-way data (CLV3W) approach which extends the clustering of variables around latent components (CLV) approach to three-way data typically obtained from a conventional sensory profiling procedure (i.e., assessors rating products on various descriptors). The CLV3W method groups the descriptors into Q clusters and estimates for each cluster an associated latent sensory component such that the attributes within each cluster are as much related (i.e., highest squared covariance) as possible with the latent component. Simultaneously, for each latent sensory component separately, a system of weights is estimated that yields information regarding the extent to which an assessor (dis)agrees with the rest of the panel according to the latent sensory component under study. Our new approach is illustrated with a dataset pertaining to Quantitative Descriptive Analysis applied to cider varieties. It is shown that CLV3W, as opposed to related approaches, is able to detect differential panel disagreement on various latent sensory components. (C) 2015 Elsevier Ltd. All rights reserved.
机译：为了检测小组意见分歧，我们建议围绕三向数据（CLV3W）的潜在变量进行聚类，将围绕潜在分量（CLV）的变量的聚类扩展到通常从常规感官分析程序获得的三向数据（即，评估人员根据各种描述符对产品进行评分）。 CLV3W方法将描述符分组为Q个聚类，并为每个聚类估计相关的潜在感觉成分，以使每个聚类中的属性与潜在成分尽可能多地相关（即，最高平方协方差）。同时，针对每个潜在的感官分量，估计一个权重系统，该权重系统得出有关评估者根据所研究的潜在的感官分量在多大程度上同意小组其他成员的信息。苹果酒品种的定量描述分析数据集说明了我们的新方法。结果表明，与相关方法相反，CLV3W能够检测出各种潜伏感官组件上的差异面板差异。 （C）2015 Elsevier Ltd.保留所有权利。

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