Discrimination Ability of Assessors in Check-All-That-Apply Tests: Method and Product Development
Binary similarity measures have been used in several research fields, but their application in sensory data analysis is limited as of yet. Since check-all-that-apply (CATA) data consist of binary answers from the participants, binary similarity measures seem to be a natural choice for their evaluati...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/10/5/1123 |
| _version_ | 1797533560629886976 |
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| author | Attila Gere Dávid Bajusz Barbara Biró Anita Rácz |
| author_facet | Attila Gere Dávid Bajusz Barbara Biró Anita Rácz |
| author_sort | Attila Gere |
| collection | DOAJ |
| description | Binary similarity measures have been used in several research fields, but their application in sensory data analysis is limited as of yet. Since check-all-that-apply (CATA) data consist of binary answers from the participants, binary similarity measures seem to be a natural choice for their evaluation. This work aims to define the discrimination ability of CATA participants by calculating the consensus values of 44 binary similarity measures. The proposed methodology consists of three steps: (i) calculating the binary similarity values of the assessors, sample pair-wise; (ii) clustering participants into good and poor discriminators based on their binary similarity values; (iii) performing correspondence analysis on the CATA data of the two clusters. Results of three case studies are presented, highlighting that a simple clustering based on the computed binary similarity measures results in higher quality correspondence analysis with more significant attributes, as well as better sample discrimination (even according to overall liking). |
| first_indexed | 2024-03-10T11:17:16Z |
| format | Article |
| id | doaj.art-96d4db1ee3ce43868ef518176b136397 |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-10T11:17:16Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-96d4db1ee3ce43868ef518176b1363972023-11-21T20:22:01ZengMDPI AGFoods2304-81582021-05-01105112310.3390/foods10051123Discrimination Ability of Assessors in Check-All-That-Apply Tests: Method and Product DevelopmentAttila Gere0Dávid Bajusz1Barbara Biró2Anita Rácz3Department of Postharvest, Supply Chain, Commerce and Sensory Science, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, Villányi út 29-43, H-1118 Budapest, HungaryMedicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar Tudósok krt. 2, H-1117 Budapest, HungaryDepartment of Postharvest, Supply Chain, Commerce and Sensory Science, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, Villányi út 29-43, H-1118 Budapest, HungaryPlasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar Tudósok krt. 2, H-1117 Budapest, HungaryBinary similarity measures have been used in several research fields, but their application in sensory data analysis is limited as of yet. Since check-all-that-apply (CATA) data consist of binary answers from the participants, binary similarity measures seem to be a natural choice for their evaluation. This work aims to define the discrimination ability of CATA participants by calculating the consensus values of 44 binary similarity measures. The proposed methodology consists of three steps: (i) calculating the binary similarity values of the assessors, sample pair-wise; (ii) clustering participants into good and poor discriminators based on their binary similarity values; (iii) performing correspondence analysis on the CATA data of the two clusters. Results of three case studies are presented, highlighting that a simple clustering based on the computed binary similarity measures results in higher quality correspondence analysis with more significant attributes, as well as better sample discrimination (even according to overall liking).https://www.mdpi.com/2304-8158/10/5/1123panelist performancediscrimination abilityCATAproduct developmentbinary similarity |
| spellingShingle | Attila Gere Dávid Bajusz Barbara Biró Anita Rácz Discrimination Ability of Assessors in Check-All-That-Apply Tests: Method and Product Development Foods panelist performance discrimination ability CATA product development binary similarity |
| title | Discrimination Ability of Assessors in Check-All-That-Apply Tests: Method and Product Development |
| title_full | Discrimination Ability of Assessors in Check-All-That-Apply Tests: Method and Product Development |
| title_fullStr | Discrimination Ability of Assessors in Check-All-That-Apply Tests: Method and Product Development |
| title_full_unstemmed | Discrimination Ability of Assessors in Check-All-That-Apply Tests: Method and Product Development |
| title_short | Discrimination Ability of Assessors in Check-All-That-Apply Tests: Method and Product Development |
| title_sort | discrimination ability of assessors in check all that apply tests method and product development |
| topic | panelist performance discrimination ability CATA product development binary similarity |
| url | https://www.mdpi.com/2304-8158/10/5/1123 |
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