Integration of Dependent Features on Sensory Evaluation Processes

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.

[1]  Herbert Stone,et al.  Sensory Evaluation Practices , 1985 .

[2]  Macarena Espinilla,et al.  A COMPARATIVE STUDY OF HETEROGENEOUS DECISION ANALYSIS APPROACHES APPLIED TO SUSTAINABLE ENERGY EVALUATION , 2012 .

[3]  Francisco Herrera,et al.  Computing with words in decision making: foundations, trends and prospects , 2009, Fuzzy Optim. Decis. Mak..

[4]  Macarena Espinilla,et al.  An Evaluation Model with Unbalanced Linguistic Information Applied to Olive Oil Sensory Evaluation , 2009, J. Multiple Valued Log. Soft Comput..

[5]  Wei Yang,et al.  New aggregation operators based on the Choquet integral and 2-tuple linguistic information , 2012, Expert Syst. Appl..

[6]  G. Klir,et al.  Fuzzy Measure Theory , 1993 .

[7]  Macarena Espinilla,et al.  Fuzzy Linguistic Olive Oil Sensory Evaluation Model based on Unbalanced Linguistic Scales , 2014, J. Multiple Valued Log. Soft Comput..

[8]  G. Choquet Theory of capacities , 1954 .

[9]  Jean-Luc Marichal,et al.  An axiomatic approach of the discrete Choquet integral as a tool to aggregate interacting criteria , 2000, IEEE Trans. Fuzzy Syst..

[10]  Robert T. Clemen,et al.  Making Hard Decisions: An Introduction to Decision Analysis , 1997 .

[11]  Garmt Dijksterhuis Multivariate data analysis in sensory and consumer science , 1997 .

[12]  Macarena Espinilla,et al.  A Linguistic Multigranular Sensory Evaluation Model for Olive Oil , 2008, Int. J. Comput. Intell. Syst..

[13]  Vicenç Torra,et al.  Modeling decisions - information fusion and aggregation operators , 2007 .

[14]  Luis Martínez-López,et al.  A linguistic decision support model for QoS priorities in networking , 2012, Knowl. Based Syst..

[15]  Macarena Espinilla,et al.  A 360-degree performance appraisal model dealing with heterogeneous information and dependent criteria , 2013, Inf. Sci..

[16]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[17]  Luis Martínez,et al.  Sensory evaluation based on linguistic decision analysis , 2007 .

[18]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[19]  Francisco Herrera,et al.  A 2-tuple fuzzy linguistic representation model for computing with words , 2000, IEEE Trans. Fuzzy Syst..

[20]  Macarena Espinilla,et al.  Using linguistic incomplete preference relations to cold start recommendations , 2010, Internet Res..

[21]  Xianyi Zeng,et al.  Intelligent Sensory Evaluation , 2004 .

[22]  Xianyi Zeng,et al.  Intelligent Sensory Evaluation: Methodologies and Applications , 2004 .