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Free JAR experiment: Data analysis and comparison with JAR task
•Free JAR and JAR protocols are compared based on a case study pertaining to cheeses.•A new strategy for the analysis of Free JAR and JAR data is introduced.•Free JAR procedure highlights specific attributes and their relative importance.•Free JAR and JAR procedures can be used jointly to elicit app...
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Published in: | Food quality and preference 2022-06, Vol.98, p.104453, Article 104453 |
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container_title | Food quality and preference |
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creator | Luc, Alexiane Lê, Sébastien Philippe, Mathilde Qannari, El Mostafa Vigneau, Evelyne |
description | •Free JAR and JAR protocols are compared based on a case study pertaining to cheeses.•A new strategy for the analysis of Free JAR and JAR data is introduced.•Free JAR procedure highlights specific attributes and their relative importance.•Free JAR and JAR procedures can be used jointly to elicit appropriate attributes.
The Free JAR protocol is introduced as a methodology for highlighting product improvement keys without imposing specific attributes to the respondents. Being relatively new, there is a need to demonstrate its relevance compared to an established sensory methodology such as the JAR procedure. The first aim of this study is to outline a strategy for the statistical analysis of Free JAR comments. This consists in extracting all the attributes assessed according to modalities such as “not enough”, “JAR” and “too much”, and recoding the Free JAR comments of each respondent into a products × attributes matrix. At a product level, an analysis is performed with the aim of highlighting the attributes that characterize the product under study. Thereafter, a multivariate descriptive analysis is presented. It consists in calculating a dissimilarity measure for each pair of products. Multidimensional Scaling (MDS) is performed on the dissimilarity matrix thus formed in order to obtain a graphical display that depicts the relationships among the products. For a comparison purpose, the same analyses are applied to JAR data. The findings based on a case study pertaining to cheeses make it possible to assess the respective shortcomings and benefits of Free JAR and JAR procedures. |
doi_str_mv | 10.1016/j.foodqual.2021.104453 |
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The Free JAR protocol is introduced as a methodology for highlighting product improvement keys without imposing specific attributes to the respondents. Being relatively new, there is a need to demonstrate its relevance compared to an established sensory methodology such as the JAR procedure. The first aim of this study is to outline a strategy for the statistical analysis of Free JAR comments. This consists in extracting all the attributes assessed according to modalities such as “not enough”, “JAR” and “too much”, and recoding the Free JAR comments of each respondent into a products × attributes matrix. At a product level, an analysis is performed with the aim of highlighting the attributes that characterize the product under study. Thereafter, a multivariate descriptive analysis is presented. It consists in calculating a dissimilarity measure for each pair of products. Multidimensional Scaling (MDS) is performed on the dissimilarity matrix thus formed in order to obtain a graphical display that depicts the relationships among the products. For a comparison purpose, the same analyses are applied to JAR data. The findings based on a case study pertaining to cheeses make it possible to assess the respective shortcomings and benefits of Free JAR and JAR procedures.</description><identifier>ISSN: 0950-3293</identifier><identifier>EISSN: 1873-6343</identifier><identifier>DOI: 10.1016/j.foodqual.2021.104453</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Cheese ; Food engineering ; Free comment ; Free JAR ; JAR ; Life Sciences ; Multidimensional scaling ; Statistics</subject><ispartof>Food quality and preference, 2022-06, Vol.98, p.104453, Article 104453</ispartof><rights>2021 Elsevier Ltd</rights><rights>Attribution - NonCommercial</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c394t-f1c364e61cd7b071933457f6ab0d80235a9cabce6efc286a3384e8e941fdeaa03</citedby><cites>FETCH-LOGICAL-c394t-f1c364e61cd7b071933457f6ab0d80235a9cabce6efc286a3384e8e941fdeaa03</cites><orcidid>0000-0003-4488-4058 ; 0000-0001-8814-6714</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://hal.science/hal-03550338$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Luc, Alexiane</creatorcontrib><creatorcontrib>Lê, Sébastien</creatorcontrib><creatorcontrib>Philippe, Mathilde</creatorcontrib><creatorcontrib>Qannari, El Mostafa</creatorcontrib><creatorcontrib>Vigneau, Evelyne</creatorcontrib><title>Free JAR experiment: Data analysis and comparison with JAR task</title><title>Food quality and preference</title><description>•Free JAR and JAR protocols are compared based on a case study pertaining to cheeses.•A new strategy for the analysis of Free JAR and JAR data is introduced.•Free JAR procedure highlights specific attributes and their relative importance.•Free JAR and JAR procedures can be used jointly to elicit appropriate attributes.
The Free JAR protocol is introduced as a methodology for highlighting product improvement keys without imposing specific attributes to the respondents. Being relatively new, there is a need to demonstrate its relevance compared to an established sensory methodology such as the JAR procedure. The first aim of this study is to outline a strategy for the statistical analysis of Free JAR comments. This consists in extracting all the attributes assessed according to modalities such as “not enough”, “JAR” and “too much”, and recoding the Free JAR comments of each respondent into a products × attributes matrix. At a product level, an analysis is performed with the aim of highlighting the attributes that characterize the product under study. Thereafter, a multivariate descriptive analysis is presented. It consists in calculating a dissimilarity measure for each pair of products. Multidimensional Scaling (MDS) is performed on the dissimilarity matrix thus formed in order to obtain a graphical display that depicts the relationships among the products. For a comparison purpose, the same analyses are applied to JAR data. The findings based on a case study pertaining to cheeses make it possible to assess the respective shortcomings and benefits of Free JAR and JAR procedures.</description><subject>Cheese</subject><subject>Food engineering</subject><subject>Free comment</subject><subject>Free JAR</subject><subject>JAR</subject><subject>Life Sciences</subject><subject>Multidimensional scaling</subject><subject>Statistics</subject><issn>0950-3293</issn><issn>1873-6343</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNqFkMFOwzAQRC0EEqXwCyhXDinr2HESLlABpaBKSAjO1tbZqC5pXOxQ6N-TEuDKaVejeaPdYeyUw4gDV-fLUeVc-faO9SiBhHeilKnYYwOeZyJWQop9NoAihVgkhThkRyEsAXgGPBmwy4knih7GTxF9rsnbFTXtRXSDLUbYYL0NNnRLGRm3WqO3wTXRh20X30SL4fWYHVRYBzr5mUP2Mrl9vp7Gs8e7--vxLDaikG1ccSOUJMVNmc0h44UQMs0qhXMoc0hEioXBuSFFlUlyhULkknIqJK9KQgQxZGd97gJrve7uRL_VDq2ejmd6p4FIU-iwDe-8qvca70LwVP0BHPSuMr3Uv5XpXWW6r6wDr3qQuk82lrwOxlJjqLSeTKtLZ_-L-ALY6Hbe</recordid><startdate>202206</startdate><enddate>202206</enddate><creator>Luc, Alexiane</creator><creator>Lê, Sébastien</creator><creator>Philippe, Mathilde</creator><creator>Qannari, El Mostafa</creator><creator>Vigneau, Evelyne</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>AAYXX</scope><scope>CITATION</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0003-4488-4058</orcidid><orcidid>https://orcid.org/0000-0001-8814-6714</orcidid></search><sort><creationdate>202206</creationdate><title>Free JAR experiment: Data analysis and comparison with JAR task</title><author>Luc, Alexiane ; Lê, Sébastien ; Philippe, Mathilde ; Qannari, El Mostafa ; Vigneau, Evelyne</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c394t-f1c364e61cd7b071933457f6ab0d80235a9cabce6efc286a3384e8e941fdeaa03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Cheese</topic><topic>Food engineering</topic><topic>Free comment</topic><topic>Free JAR</topic><topic>JAR</topic><topic>Life Sciences</topic><topic>Multidimensional scaling</topic><topic>Statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Luc, Alexiane</creatorcontrib><creatorcontrib>Lê, Sébastien</creatorcontrib><creatorcontrib>Philippe, Mathilde</creatorcontrib><creatorcontrib>Qannari, El Mostafa</creatorcontrib><creatorcontrib>Vigneau, Evelyne</creatorcontrib><collection>CrossRef</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Food quality and preference</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Luc, Alexiane</au><au>Lê, Sébastien</au><au>Philippe, Mathilde</au><au>Qannari, El Mostafa</au><au>Vigneau, Evelyne</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Free JAR experiment: Data analysis and comparison with JAR task</atitle><jtitle>Food quality and preference</jtitle><date>2022-06</date><risdate>2022</risdate><volume>98</volume><spage>104453</spage><pages>104453-</pages><artnum>104453</artnum><issn>0950-3293</issn><eissn>1873-6343</eissn><abstract>•Free JAR and JAR protocols are compared based on a case study pertaining to cheeses.•A new strategy for the analysis of Free JAR and JAR data is introduced.•Free JAR procedure highlights specific attributes and their relative importance.•Free JAR and JAR procedures can be used jointly to elicit appropriate attributes.
The Free JAR protocol is introduced as a methodology for highlighting product improvement keys without imposing specific attributes to the respondents. Being relatively new, there is a need to demonstrate its relevance compared to an established sensory methodology such as the JAR procedure. The first aim of this study is to outline a strategy for the statistical analysis of Free JAR comments. This consists in extracting all the attributes assessed according to modalities such as “not enough”, “JAR” and “too much”, and recoding the Free JAR comments of each respondent into a products × attributes matrix. At a product level, an analysis is performed with the aim of highlighting the attributes that characterize the product under study. Thereafter, a multivariate descriptive analysis is presented. It consists in calculating a dissimilarity measure for each pair of products. Multidimensional Scaling (MDS) is performed on the dissimilarity matrix thus formed in order to obtain a graphical display that depicts the relationships among the products. For a comparison purpose, the same analyses are applied to JAR data. The findings based on a case study pertaining to cheeses make it possible to assess the respective shortcomings and benefits of Free JAR and JAR procedures.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.foodqual.2021.104453</doi><orcidid>https://orcid.org/0000-0003-4488-4058</orcidid><orcidid>https://orcid.org/0000-0001-8814-6714</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Cheese Food engineering Free comment Free JAR JAR Life Sciences Multidimensional scaling Statistics |
title | Free JAR experiment: Data analysis and comparison with JAR task |
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