Technique for order of preference by similarity to ideal solution (TOPSIS) method for the generation of external preference mapping using rapid sensometric techniques.


Journal

Journal of the science of food and agriculture
ISSN: 1097-0010
Titre abrégé: J Sci Food Agric
Pays: England
ID NLM: 0376334

Informations de publication

Date de publication:
Jun 2021
Historique:
revised: 13 11 2020
received: 17 04 2020
accepted: 22 11 2020
pubmed: 23 11 2020
medline: 17 7 2021
entrez: 22 11 2020
Statut: ppublish

Résumé

External preference mapping is a powerful tool to explain consumer preference or rejection. Combining the technique for order of preference by similarity to ideal solution (TOPSIS) multicriteria analysis with rapid descriptive techniques can improve preference map (PREFMAP) results. This study was conducted to compare the PREFMAPs generated with rapid descriptive flash profile (FP), check-all-that-apply (CATA), and Napping® versus PREFMAPs constructed with FP-TOPSIS, CATA-TOPSIS, and Napping-TOPSIS. Only 38.46%, 63.66%, and 42% of sensory attributes initially generated by FP, CATA, and Napping techniques respectively were considered for the determination of their weight W and allocation as positive or negative in the TOPSIS technique. The PREFMAPs constructed with FP-TOPSIS, CATA-TOPSIS, and Napping-TOPSIS presented a better explanation of the preference and rejection than the PREFMAPs directly generated with rapid sensory techniques. The results of the multiple factor analysis and coefficient Rv indicated similarities in the sensory vocabularies used after the TOPSIS technique. The combination of the TOPSIS technique with rapid sensory techniques is a reliable alternative for the construction of PREFMAPs in order to identify the sensory attributes responsible for preference and rejection of food products. © 2020 Society of Chemical Industry.

Sections du résumé

BACKGROUND BACKGROUND
External preference mapping is a powerful tool to explain consumer preference or rejection. Combining the technique for order of preference by similarity to ideal solution (TOPSIS) multicriteria analysis with rapid descriptive techniques can improve preference map (PREFMAP) results. This study was conducted to compare the PREFMAPs generated with rapid descriptive flash profile (FP), check-all-that-apply (CATA), and Napping® versus PREFMAPs constructed with FP-TOPSIS, CATA-TOPSIS, and Napping-TOPSIS.
RESULTS RESULTS
Only 38.46%, 63.66%, and 42% of sensory attributes initially generated by FP, CATA, and Napping techniques respectively were considered for the determination of their weight W and allocation as positive or negative in the TOPSIS technique. The PREFMAPs constructed with FP-TOPSIS, CATA-TOPSIS, and Napping-TOPSIS presented a better explanation of the preference and rejection than the PREFMAPs directly generated with rapid sensory techniques. The results of the multiple factor analysis and coefficient Rv indicated similarities in the sensory vocabularies used after the TOPSIS technique.
CONCLUSION CONCLUSIONS
The combination of the TOPSIS technique with rapid sensory techniques is a reliable alternative for the construction of PREFMAPs in order to identify the sensory attributes responsible for preference and rejection of food products. © 2020 Society of Chemical Industry.

Identifiants

pubmed: 33222200
doi: 10.1002/jsfa.10959
doi:

Substances chimiques

Coffee 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

3298-3307

Informations de copyright

© 2020 Society of Chemical Industry.

Références

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Auteurs

Lorena G Ramón-Canul (LG)

Ingeniería en Innovación Agricola Sustentable, Tecnológico Nacional de México/Instituto Tecnológico de Mérida, Mérida, México.
Centro de Investigación en Nutrición y Alimentación, Universidad de la Sierra Sur, Miahuatlan de Porfirio Diaz, México.

Diana L Margarito-Carrizal (DL)

Ingeniería en Innovación Agricola Sustentable, Tecnológico Nacional de México/Instituto Tecnológico Superior de Zongolica, Veracruz, México.

Rogelio Limón-Rivera (R)

Ingeniería en Innovación Agricola Sustentable, Tecnológico Nacional de México/Instituto Tecnológico Superior de Zongolica, Veracruz, México.

Uriel A Morales-Carrrera (UA)

Ingeniería en Innovación Agricola Sustentable, Tecnológico Nacional de México/Instituto Tecnológico Superior de Zongolica, Veracruz, México.

Ingrid M Rodríguez-Buenfil (IM)

Sede Sureste, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco A.C., Mérida, México.

Manuel O Ramírez-Sucre (MO)

Sede Sureste, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco A.C., Mérida, México.

Adán Cabal-Prieto (A)

Maestría en Ingeniería, Tecnológico Nacional de México/Instituto Tecnológico Superior de Huatusco, Veracruz, México.

José A Herrera-Corredor (JA)

Campus Córdoba, Colegio de Postgraduados, Veracruz, México.

Emmanuel de Jesús Ramírez-Rivera (E)

Ingeniería en Innovación Agricola Sustentable, Tecnológico Nacional de México/Instituto Tecnológico Superior de Zongolica, Veracruz, México.
Sede Sureste, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco A.C., Mérida, México.

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