Comparison of Check-All-That-Apply and Adapted-Pivot-Test methods for wine sensory characterization with a panel of untrained students
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- handle: 10670/1.wnmtzs
- hal: hal-04091312
- ARXIV: 2305.06211
- doi: 10.1111/joss.12862
- WOS: 001026205500001
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info:eu-repo/semantics/altIdentifier/arxiv/2305.06211
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info:eu-repo/semantics/altIdentifier/doi/10.1111/joss.12862
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consumers paired comparison wine Chi-square test of independence correspondence analysis -bio]/Agricultural sciences/Sciences and technics of agricultureSujets proches
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Sylvain Nougarède et al., « Comparison of Check-All-That-Apply and Adapted-Pivot-Test methods for wine sensory characterization with a panel of untrained students », Archive Ouverte d'INRAE, ID : 10.1111/joss.12862
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Résumé
: The Check-All-That-Apply (CATA) method was compared with 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 results showed that CATA was more robust and more descriptive than the APT with 50–60 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 set-up 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 sensory characterization 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.