Abstract When considering sensory discrimination studies, multiple d-prime values are often obtained from several sensory attributes. In this paper, we introduce principal component analysis as a way of gaining information about d-prime values across sensory attributes. Specifically, we propose estimating d-prime values using a Thurstonian mixed model for binary paired comparison data and then using these estimates in a principal component analysis. Binary paired comparisons are a sensitive way to test products with only subtle differences. When analyzing data with a Thurstonian mixed model, product-specific as well as assessor-specific d-prime values are obtained. Principal component analysis of these values results in information about products and assessors across multiple sensory attributes illustrated by product and attribute maps. Furthermore, the analysis captures individual differences. Thus, by using d-prime values from a multi-attribute 2-AFC study in principal component analysis insights that are typically obtained considering quantitative descriptive analysis are obtained.

中文翻译：

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使用二元配对比较对感官辨别测试中的 d-prime 值进行主成分分析

摘要 在考虑感官辨别研究时，通常从多个感官属性中获得多个 d-prime 值。在本文中，我们介绍了主成分分析，作为获取有关跨感官属性的 d-prime 值信息的一种方式。具体来说，我们建议使用 Thurstonian 混合模型对二元配对比较数据估计 d-prime 值，然后在主成分分析中使用这些估计值。二元配对比较是测试只有细微差别的产品的敏感方法。使用 Thurstonian 混合模型分析数据时，可以获得特定于产品和特定于评估者的 d-prime 值。对这些值的主成分分析会产生关于产品和评估者的信息，这些信息跨越由产品和属性图说明的多个感官属性。此外，分析捕获了个体差异。因此，通过在主成分分析中使用多属性 2-AFC 研究中的 d-prime 值，可以获得通常考虑定量描述性分析而获得的见解。