Integration of Dependent Features on Sensory Evaluation Processes

  • Macarena Espinilla
  • Francisco J. Martínez
  • Francisco Javier Estrella Liébana
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8536)


The aim of a sensory evaluation process is to compute the global value of each evaluated product by means of an evaluator set, according to a set of sensory features. Several sensory evaluation models have been proposed which use classical aggregation operators to summary the sensory information, assuming independent sensory features, i.e, there is not interaction among them. However, the sensory information is perceived by the set of human senses and, depending on the evaluated product, its sensory features may be dependent and present interaction among them. In this contribution, we present the integration of dependent sensory features in sensory evaluation processes. To do so, we propose the use of the fuzzy measure in conjunction with the Choquet integral to deal with this dependence, extending a sensory evaluation model proposed in the literature. This sensory evaluation model has the advantage that offers linguistic terms to handle the uncertainty and imprecision involved in evaluation sensory processes. Finally, an illustrative example of a sensory evaluation process with dependent sensory features is shown.


Sensory evaluation decision analysis sensory information linguistic information interaction dependence 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Choquet, G.: Theory of capacities, vol. 5, pp. 131–295. Annales de l’institut Fourier (1953)Google Scholar
  2. 2.
    Clemen, R.T.: Making Hard Decisions. An Introduction to Decision Analisys. Duxbury Press (1995)Google Scholar
  3. 3.
    Dijksterhuis, G.B.: Multivariate Data Analysis in Sensory and Consumer Science, Food and Nutrition. Press Inc., Trumbull (1997)Google Scholar
  4. 4.
    Espinilla, M., de Andrés, R., Martínez, F.J., Martínez, L.: A 360-degree performance appraisal model dealing with heterogeneous information and dependent criteria. Information Sciences 222, 459–471 (2013)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Espinilla, M., Palomares, I., Martínez, L., Ruan, D.: A comparative study of heterogeneous decision analysis approaches applied to sustainable energy evaluation. International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems 20(suppl. 1), 159–174 (2012)CrossRefGoogle Scholar
  6. 6.
    Estrella, F.J., Espinilla, M., Martínez, L.: Fuzzy linguistic olive oil sensory evaluation model based on unbalanced linguistic scales. Journal of Multiple-Valued Logic and Soft Computing 22, 501–520 (2014)Google Scholar
  7. 7.
    Gramajo, S., Martínez, L.: A linguistic decision support model for QoS priorities in networking. Knowledge-Based Systems 32(1), 65–75 (2012)Google Scholar
  8. 8.
    Herrera, F., Alonso, S., Chiclana, F., Herrera-Viedma, E.: Computing with words in decision making: foundations, trends and prospects. Fuzzy Optimization and Decision Making 8, 337–364 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Herrera, F., Martínez, L.: A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems 8(6), 746–752 (2000)CrossRefGoogle Scholar
  10. 10.
    Marichal, J.-L.: An axiomatic approach of the discrete choquet integral as a tool to aggregate interacting criteria. IEEE Transactions on Fuzzy Systems 8(6), 800–807 (2000)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Martínez, L.: Sensory evaluation based on linguistic decision analysis. International Journal of Approximate Reasoning 44(2), 148–164 (2007)CrossRefGoogle Scholar
  12. 12.
    Martínez, L., Espinilla, M., Liu, J., Pérez, L.G., Sánchez, P.J.: An evaluation model with unbalanced linguistic information applied to olive oil sensory evaluation. Journal of Multiple-Valued Logic and Soft Computing 15(2-3), 229–251 (2009)Google Scholar
  13. 13.
    Martínez, L., Espinilla, M., Pérez, L.G.: A linguistic multigranular sensory evaluation model for olive oil. International Journal of Computational Intelligence Systems 1(2), 148–158 (2008)CrossRefGoogle Scholar
  14. 14.
    Rodríguez, R.M., Espinilla, M., Sánchez, P.J., Martínez, L.: Using linguistic incomplete preference relations to cold start recommendations. Internet Research 20(3), 296–315 (2010)CrossRefGoogle Scholar
  15. 15.
    Ruan, D., Zeng, X. (eds.): Intelligent Sensory Evaluation: Methodologies and Applications. Springer (2004)Google Scholar
  16. 16.
    Stone, H., Sidel, J.L.: Sensory Evaluation Practice. Academic Press Inc., San Diego (1993)Google Scholar
  17. 17.
    Torra, V., Narukawa, Y.: Modeling decisions: Information fusion and aggregation operators. Cognitive Technologies 13 (2007)Google Scholar
  18. 18.
    Wang, Z., Klir, G.: Fuzzy measure theory. Plenum Press, New York (1992)Google Scholar
  19. 19.
    Yager, R.R.: On ordered weighted averaging operators in multicriteria decision making. IEEE Transactions on Systems, Man, and Cybernetics 18, 183–190 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Yang, W., Chen, Z.: New aggregation operators based on the choquet integral and 2-tuple linguistic information. Expert Systems with Applications 39(3), 2662–2668 (2012)CrossRefGoogle Scholar
  21. 21.
    Zadeh, L.A.: The concept of a linguistic variable and its applications to approximate reasoning. Information Sciences, Part I, II, III, 8,8,9:199–249,301–357,43–80 (1975)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Macarena Espinilla
    • 1
  • Francisco J. Martínez
    • 1
  • Francisco Javier Estrella Liébana
    • 1
  1. 1.Department of Computer ScienceUniversity of JaénJaénSpain

Personalised recommendations