Abstract The free-sorting task is increasingly popular as a rapid sensory method to give a global picture of the similarities among samples. Sorting does not require training analysts, allows for the easy, simultaneous presentation of up to 20 samples, and provides stable results with 25–30 subjects. However, wide use of free-sorting is hindered by the current analyses for free sorting—for example DISTATIS and Correspondence Analysis—which require statistical expertise to conduct and interpret. In this paper a novel, alternative analysis is proposed, called “Sorting Backbone Analysis” (SBA), which is based on tools from network analysis. The similarity data produced from free sorting can represent a weighted network, and so a set of network-analysis tools can be used to identify groups of products which are significantly similar, and to visualize these results clearly and powerfully. SBA is simple and can be implemented with open-source software, provides interpretations that agree with current methods, and produces clear, powerful visualizations called “graphs,” which may offer new, interpretable insights to sensory scientists. This paper describes the mathematical and statistical background for SBA and applies SBA to four, previously published sorting datasets, with comparisons to DISTATIS. In each case SBA produces visual results that highlight all of the same features as the standard approach while being easier to interpret, and in many cases produces new insights. Therefore, SBA specifically and network analysis in general are suggested as new approaches for use in the analysis of sensory similarity data as produced through free sorting and related methods.

中文翻译：

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排序主干分析：一种从自由排序任务结果中提取关键可操作信息的基于网络的方法

摘要 自由分类任务作为一种快速的感官方法越来越流行，可以给出样本之间相似性的全局图。排序不需要培训分析师，可以轻松、同时呈现多达 20 个样本，并为 25-30 个受试者提供稳定的结果。然而，当前的自由排序分析（例如 DISTATIS 和对应分析）阻碍了自由排序的广泛使用，这些分析需要统计专业知识来进行和解释。在本文中，提出了一种新颖的替代分析，称为“排序主干分析”（SBA），它基于网络分析工具。自由排序产生的相似性数据可以表示一个加权网络，因此可以使用一组网络分析工具来识别显着相似的产品组，并清晰有力地可视化这些结果。SBA 很简单，可以用开源软件实现，提供与当前方法一致的解释，并产生称为“图表”的清晰、强大的可视化，这可能为感官科学家提供新的、可解释的见解。本文描述了 SBA 的数学和统计背景，并将 SBA 应用于四个以前发布的排序数据集，并与 DISTATIS 进行了比较。在每种情况下，SBA 都会产生视觉结果，突出显示与标准方法相同的所有特征，同时更易于解释，并且在许多情况下产生新的见解。因此，特别是 SBA 和一般的网络分析被建议作为用于分析通过自由排序和相关方法产生的感官相似性数据的新方法。