The Check-All-That-Apply (CATA) method was compared to the Adapted-Pivot-Test (APT) method, a recently published method based on pair comparisons between a coded wine and a reference sample, called pivot, and using a set list of attributes as in CATA. Both methods were compared using identical wines, correspondence analyses and Chi-square test of independence, and very similar questionnaires. The list of attributes used for describing the wines was established in a prior analysis by a subset of the panel. The results showed that CATA was more robust and more descriptive than the APT with 50 to 60 panelists. The p-value of the Chi-square test of independence between wines and descriptors dropped below 0.05 around 50 panelists with the CATA method, when it never dropped below 0.8 with the APT. The discussion highlights differences in settings and logistics which render the CATA more robust and easier to run. One of the objectives was also to propose an easy setup for university and food industry laboratories. Practical applications: Our results describe a practical way of teaching and performing the CATA method with university students and online tools, as well as in extension courses. It should have applications with consumer studies for the characterization of various food products. Additionally, we provide an improved R script for correspondence analyses used in descriptive analyses and a Chi-square test to estimate the number of panelists leading to robust results. Finally, we give a set of data that could be useful for sensory and statistics teaching.

翻译：“勾选所有适用项”（CATA）方法与“适应性支点测试”（APT）方法进行了比较。APT是一种近期发表的方法，基于编码酒样与称为支点的参考样品之间的配对比较，并使用与CATA中相同的属性列表。两种方法采用相同的葡萄酒、对应分析和卡方独立性检验，以及非常相似的问卷进行评估。用于描述葡萄酒的属性列表由小组中的部分成员通过事先分析确定。结果表明，在50至60名评测员的情况下，CATA比APT更稳健且更具描述性。在CATA方法中，葡萄酒与描述词之间的卡方独立性检验p值大约在50名评测员时降至0.05以下，而APT方法的p值从未低于0.8。讨论部分强调了设置和操作流程上的差异，这些差异使得CATA更加稳健且易于执行。目标之一也是为大学和食品行业实验室提供简便的设置方案。实际应用：我们的结果描述了在大学学生及在线工具，以及推广课程中教授和执行CATA方法的实用途径。该方法应在消费者研究中用于各类食品特性的描述。此外，我们为描述性分析中使用的对应分析提供了一个改进的R脚本，并采用卡方检验来估算获得稳健结果所需的评测员数量。最后，我们提供了一组可用于感官科学和统计学教学的数据。