A New Unbalanced Linguistic Scale for the Classification of Olive Oil Based on the Fuzzy Linguistic Approach

  • M. EspinillaEmail author
  • F. J. Estrella
  • L. Martínez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 213)


A key factor that determines the price of olive oil is its sensory profile. The International Olive Council (IOC) establishes four quality categories and a method to classify a sample of olive oil into one category, depending on its sensory characteristics. To do so, a taster panel is rigorously trained to provide the intensity perceived on a 10-cm scale for each organoleptic characteristic. These intensities are aggregated and analyzed statistically to obtain the classification among one of four quality categories established. The modeling and management of perceptions in sensory evaluation processes is an important problem because the information acquired by human senses always involves imprecision and uncertainty that has a non-probabilistic nature. The application of the fuzzy linguistic approach to sensory evaluation processes can model and manage the uncertainty and vagueness of this kind of processes. The main challenge in this approach is to establish a linguistic scale to measure tasters’ perceptions, since the success or failure of the sensory evaluation process will depend on the definition of a proper scale. In this contribution is analyzed and proposed an unbalanced linguistic scale to carry out the classification of olive oil samples, such a scale is validated, conducting a sensory evaluation case study for olive oil.


Sensory evaluation Fuzzy linguistic approach Unbalanced linguistic scale Olive oil Linguistic 2-tuple. 



This contribution has been supported by the research project AGR-6487.


  1. 1.
    Dijksterhuis G (1997) Multivariate data analysis in sensory and consumer science. Food and Nutrition (Press Inc.), Trumbull, Connecticut, USAGoogle Scholar
  2. 2.
    Ruan D, Zeng X (eds) (2004) Intelligent sensory evaluation: methodologies and applications. Springer, New YorkGoogle Scholar
  3. 3.
    Martínez L (2007) Sensory evaluation based on linguistic decision analysis. Int. J. Approximate Reasoning 44(2):148–164CrossRefGoogle Scholar
  4. 4.
    Zadeh L (1975) The concept of a linguistic variable and its applications to approximate reasoning. Inform Sci, Part I, II, III, vol 8, 9, pp 199–249, 301–357, 43–80Google Scholar
  5. 5.
    Zadeh L (1965) Fuzzy sets. Inform Control 8(3):338–353MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Chen Y, Zeng X, Happiette M, Bruniaux P, Ng R, Yu W (2009) Optimisation of garment design using fuzzy logic and sensory evaluation techniques. Eng Appl Artif Intell 22(2):272–282CrossRefGoogle Scholar
  7. 7.
    Perrot N, Ioannou I, Allais I, Curt C, Hossenlopp J, Trystram G (2006) Fuzzy concepts applied to food product quality control: a review. Fuzzy Sets Syst 157(9):1145–1154MathSciNetCrossRefGoogle Scholar
  8. 8.
    Jaya S, Das H (2003) Sensory evaluation of mango drinks using fuzzy logic. J Sens Stud 18(2):163–176CrossRefGoogle Scholar
  9. 9.
    Sinija V, Mishra H (2011) Fuzzy analysis of sensory data for quality evaluation and ranking of instant green tea powder and granules. Food Bioprocess Technol 4(3):408–416CrossRefGoogle Scholar
  10. 10.
    Russo L, Albanese D, Siettos C, di Matteo M, Crescitelli S (2012) A neuro-fuzzy computational approach for multicriteria optimisation of the quality of espresso coffee by pod based on the extraction time, temperature and blend. Int J Food Sci Technol 47(4):837–846CrossRefGoogle Scholar
  11. 11.
    Lee S, Kwon YA (2007) Study on fuzzy reasoning application for sensory evaluation of sausages. Food Control 18(7):811–816MathSciNetCrossRefGoogle Scholar
  12. 12.
    Routray W, Mishra H (2012) Sensory evaluation of different drinks formulated from dahi (indian yogurt) powder using fuzzy logic. J Food Processing Preserv 36(1):1–10CrossRefGoogle Scholar
  13. 13.
    Martínez L, Espinilla M, Liu J, Pérez L, Sánchez P (2009) An evaluation model with unbalanced linguistic information applied to olive oil sensory evaluation. J Multiple Valued Logic Soft Comput 15(2–3):229–251Google Scholar
  14. 14.
    Blery E, Sfetsiou E (2008) Marketing olive oil in Greece. Br Food J 110(11):1150–1162CrossRefGoogle Scholar
  15. 15.
    de Graaff J, Duran Zuazo V-H, Jones N, Fleskens L (2008) Olive production systems on sloping land: prospects and scenarios. J Environ Manage 89(2):129–139CrossRefGoogle Scholar
  16. 16.
    Herrera F, Herrera-Viedma E, Martínez L (2008) A fuzzy linguistic methodology to deal with unbalanced linguistic term sets. IEEE Trans Fuzzy Syst 16(2):354–370CrossRefGoogle Scholar
  17. 17.
    Martínez L, Herrera F (2012) An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges. Inf Sci 207(1):1–18CrossRefGoogle Scholar
  18. 18.
    Martínez L, Ruan D, Herrera F (2010) Computing with words in decision support systems: an overview on models and applications. Int J Comput Intell Syst 3(4):382–395Google Scholar
  19. 19.
    Herrera F, Martínez L (2000) A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans Fuzzy Syst 8(6):746–752CrossRefGoogle Scholar
  20. 20.
    Wei G (2011) Some harmonic aggregation operators with 2-tuple linguistic assessment information and their application to multiple attribute group decision making. Int J Uncertainty Fuzziness Knowl Based Syst 19(6):977–998CrossRefzbMATHGoogle Scholar
  21. 21.
    Xu Y, Wang H (2011) Approaches based on 2-tuple linguistic power aggregation operators for multiple attribute group decision making under linguistic environment. Appl Soft Comput J 11(5):3988–3997CrossRefGoogle Scholar
  22. 22.
    Yang W, Chen Z (2012) New aggregation operators based on the choquet integral and 2-tuple linguistic information. Expert Syst Appl 39(3):2662–2668CrossRefGoogle Scholar
  23. 23.
    Herrera F, Martínez L (2001) A model based on linguistic 2-tuples for dealing with multigranularity hierarchical linguistic contexts in multiexpert decision-making. IEEE Trans Syst Man Cybern B Cybern 31(2):227–234CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Computer SciencesUniversity of JaenJaénSpain

Personalised recommendations