Datos textuales como elementos activos en sensometría = Textual data as active elements in sensometry

Ramón Álvarez Esteban; Pedro Aguado Rodríguez

doi:10.18002/pec.v0i2012.1106

Autori

Ramón Álvarez Esteban Universidad de León. Facultad de Ciencias Económicas y Empresariales, Departamento de Economía y Estadística, Área de Estadística e Investigación Operativa
Pedro Aguado Rodríguez Universidad de León. Escuela Superior y Técnica de Ingeniería Agraria. Departamento de Ingeniería y Ciencias Agrarias, Área de Ingeniería Agroforestal

DOI:

https://doi.org/10.18002/pec.v0i2012.1106

Parole chiave:

Datos textuales, Sensometría, Análisis de Correspondencias, Análisis Factorial Múltiple, Textual data, Sensometry, Correspondence Analysis, Factorial Multiple Analysis

Abstract

La utilización de datos textuales en estudios estadísticos sobre sensometría generalmente se ha realizado tratando de explicar e interpretar los resultados alcanzados a partir de datos cuantitativos. Este trabajo muestra una metodología que permite utilizar datos textuales como elementos activos. Dos catas de vinos ilustran el procedimiento.

The use of textual data in statistical studies into sensometric field has been conducted generally seeking to explain and interpret results obtained from quantitative data. This work shows a methodology that allows use textual data as active elements. Two wine tastings illustrate the procedure.

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Riferimenti bibliografici

Abdi, H., Valentin, D., Chollet, D. y Chrea, C. (2007). Analyzing assessors and products in sorting tasks: DISTATIS, theory and applications. Food Quality and Preference, 18, 627-640.

Berglund, B., Berglund, U., Engen, T. y Ekman, G. (1973). Multidimensional analysis of twenty-one odors. Scandinavian Journal of Psychology, 14, 131-137.

Brochet, F. y Dubourdieu, D. (2001). Wine Descriptive Language Supports Cognitive Specificity of Chemical Senses. Brain and Language, 77(2), 187-196.

Campo, E., Ballester, J., Langlois, J., Dacremont, C. y Valentin, D. (2010). Comparison of conventional descriptive analysis and a citation frequency-based descriptive method for odor profiling: An application to Burgundy Pinot noir wines. Food Quality and Preference, 21, 44-55.

Delarue, J. y Sieffermann, J-M. (2004). Sensory mapping using Flash profile. Comparison with a conventional descriptive method for the evaluation of the flavour of fruit dairy products. Food Quality and Preference, 15, 383-392.

Escofier, B. y Pagès, J. (1990). Analyses factorielles simples et multiples: Objectifs, méthodes, interprétations. Paris: Dunod.

Escofier, B. y Pagès, J. (1994). Multiple factor analysis (AFMULT package). Computational Statistics and Data Analysis, 18, 121-140.

Gower, J.C. y Dijksterhuis, G.B. (2004). Procrustes problems. Oxford University Press.

Husson, F., Lê, S. y Mazet, J. (2007). FactoMineR: Factor Analysis and Data Mining with R.R package version 2.4.0, URL http://factominer.free.fr/ (accessed September 2010).

Josse, J., Pagès, J. y Husson, F. (2008). Testing the significance of the RV coefficient.

Computational Statistics and Data Analysis, 53, 82-91.

Krzanowski, W.J. (1990). Principles of multivariate analysis, a user's perspective. Oxford Statistical Science Series.

Lawless, H.T., Sheng, N. y Knoops, S. (1995). Multidimensional-scaling of sorting data applied to cheese perception. Food Quality and Preference, 6, 91-98.

Labbé, D. (1990). Normes de dépouillement et procédures d’analyse des textes politiques, CERAT.

Labbé, D., Rytz, A. y Hugi, A. (2004). Training is a critical step to obtain reliable product profiles in a real food industry context. Food Quality and Preference, 15, 341-348.

Lebart, L., Salem, A. y Berry, L. (1998). Exploring textual data. Dordrecht, Boston: Kluwer Academic Publisher.

Lelièvre, M., Chollet, S., Abdi, H. y Valentin, D. (2008). What is the validity of the sorting task for describing beers? A study using trained and untrained assessors. Food Quality and Preference, 19, 697-703.

Pagès, J. (2003). Recueil direct de distances sensorielles: Application à l’évaluation de dix vins blancs du Val-de-Loire. Sciences des Aliments, 23, 679-688.

Pagès, J. (2005). Collection and analysis of perceived product interdistances using multiple factor analysis: application to the study of 10 white wines from the Loire Valley. Food Quality and Preference, 16(7), 642-649.

Perrin, L. y Pagès, J. (2009). Construction of a product space from the ultra-flash profiling method: application to 10 red wines from the Loire Valley. Journal of Sensory Studies, 24(3), 372-395.

Sauvageot, F., Urdapilleta, I. y Peyron, D. (2006). Within and between variations of texts elicited from nine wine experts. Food Quality and Preference, 17(6), 429-444.

Takane, Y. (1980). Analysis of categorizing behavior by a quantification method. Behaviormetrika, 8, 75-86.

Takane, Y. (1982). IDSORT: An individual differences multidimensional scaling for sorting data.

Behavior Research Methods and Instrumentation, 14, 546.

Williams, E.J. (1949). Experimental designs balanced for the estimation of residual effects of treatments. Australian Journal of Scientific Research, Ser. A 2, 149-168.