Technique for order of preference by similarity to ideal solution (TOPSIS) method for the generation of external preference mapping using rapid sensometric techniques
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...
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Veröffentlicht in: | Journal of the science of food and agriculture 2021-06, Vol.101 (8), p.3298-3307 |
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Hauptverfasser: | , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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
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
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 |
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ISSN: | 0022-5142 1097-0010 |
DOI: | 10.1002/jsfa.10959 |