Seleção de atributos em avaliações sensoriais descritivas
Attribute selection in descriptive sensory analysis
Rossini, Karina; Anzanello, Michel José; Fogliatto, Flavio Sanson
http://dx.doi.org/10.1590/S0103-65132012005000032
Prod, vol.22, n3, p.380-390, 2012
Resumo
A seleção dos atributos a serem avaliados em uma análise sensorial é fundamental no planejamento de painéis sensoriais. O processo de seleção visa reduzir a lista de atributos a serem apresentados aos julgadores, evitando assim fadiga aos membros do painel, porém mantendo atributos significativos na caracterização das amostras avaliadas. Este artigo apresenta um método para seleção de atributos em painéis sensoriais baseados em avaliações descritivas das amostras, tais como os métodos QDA (Quantitative Descriptive Analysis) e Spectrum. O método proposto utiliza Análise de Componentes Principais para identificação dos atributos mais relevantes e então aplica Análise Discriminante para classificação das amostras em formulações distintas. O método é aplicado em um estudo de caso em que cubos de carne com molho são caracterizados em painel sensorial utilizando o método QDA. O método proposto reduz significativamente o número de atributos a serem avaliados e conduz à satisfatória acurácia de classificação das amostras em formulações.
Palavras-chave
Seleção de atributos. Análise sensorial. Análise multivariada.
Abstract
The selection of attributes from a group of candidates to be assessed through sensory analysis is an important step when planning sensory panels. When selecting attributes, it is desirable to reduce the list of those to be presented to panelists in order to avoid fatigue, however keeping the attributes that are relevant to the sensory characterization of samples. This paper presents a multivariate method for attribute selection in descriptive sensory panels, such as those used in the QDA (Quantitative Descriptive Analysis) and Spectrum protocols. The proposed method is implemented using Principal Component Analysis and Descriptive Analysis, and it is evaluated in a case study where beef cubes in stew, used as combat ration by the American Army, are characterized in sensory panels through the Spectrum method. The method significantly reduced the number of attributes to be considered in sensory panels, while yielding satisfactory accuracy in the classification of samples.
Keywords
Attribute selection. Sensory evaluation. Multivariate analysis.
References
ANZALDÚA-MORALES, A. A. La evaluación sensorial de los alimentos en la teoria e la práctica. Zaragoza: Editorial Acribia S.A., 1994.
ANZANELLO, M. J. Seleção de variáveis com vistas à classificação de bateladas de produção em duas classes. Gestão & Produção, v. 16, p. 526-533, 2009.
ANZANELLO, M. J.; ALBIN, S.; CHAOVALITWONGSE, W. A., Selecting the best variables for classifying production batches into two quality levels. Chemometrics and Intelligent Laboratory Systems, v. 97, p. 111-117, 2009. http://dx.doi.org/10.1016/j.chemolab.2009.03.004
CARBONELL, L.; IZQUIERDO, L.; CARBONELL, I. Sensory analysis of Spanish mandarin juices. Selection of attributes and panel performance. Food Quality and Preference, v. 18, p. 329-341, 2007. http://dx.doi.org/10.1016/j.foodqual.2006.02.008
CARBONELL, L. et al. Segmentation of food consumers according to their correlations with sensory attributes projected on preference spaces. Food Quality and Preference, v. 19, p. 71-78, 2008. http://dx.doi.org/10.1016/j.foodqual.2007.06.006
DIJKSTERHUIS, G.; FRØST, M. B.; BYRNE, D. V. Selection of a subset of variables: minimisation of Procrustes loss between a subset and the full set. Food Quality and Preference, v. 13, p. 89-97, 2002. http://dx.doi.org/10.1016/S0950-3293(01)00065-9
DUDA, R.; HART, P.; STORK, D. Pattern Classification. 2. ed. New York: Wiley-Interscience, 2001.
DUTCOSKY, S. D. Análise Sensorial de Alimentos. Curitiba: Champagnat, 1996.
ESTEBAN-DÍEZ, I.; GONZÁLEZ-SÁIZ, J. M.; PIZARRO, C. Prediction of sensory properties of espresso from roasted coffee samples by near-infrared spectroscopy. Analytica Chimica Acta, v. 525, p. 171-182, 2004. http://dx.doi.org/10.1016/j.aca.2004.08.057
FOGLIATTO, F. S.; ALBIN, S. L. A hierarchical method for evaluating products with quantitative and sensory characteristics. IIE Transactions, v. 33, p. 1081-1092, 2001. http://dx.doi.org/10.1080/07408170108936898
GRANITTO, P. M. et al. Modern data mining tools in descriptive sensory analysis: A case study with a Random forest approach. Food Quality and Preference, v. 18, p. 681-689, 2007. http://dx.doi.org/10.1016/j.foodqual.2006.11.001
HAIR, J. F. et al. Análise multivariada de dados. New York: Bookman, 2006.
HEENAN, S. P. et al. Characterization of fresh bread flavor: Relationships between sensory characteristics and volatile composition. Food Chemistry, v. 116, p. 249-257, 2009. http://dx.doi.org/10.1016/j.foodchem.2009.02.042
LAWLESS, H. T.; HEYMANN, H. Sensory Evaluation of Food: Principles and Practices. New York: Chapman & Hall, 1998.
JACKSON, J. E. Principal Component and factor Analysis: Part I - Principal Components. Journal of Quality Technology, v. 12, p. 201-213, 1980.
JACKSON, J. E. Principal Component and factor Analysis: Part II - Additional Topics Related to Principal Components. Journal of Quality Technology, v. 13, p. 46-58, 1981.
JOBSON, J. D. Applied multivariate data analysis. New York: Springer-Verlag, 1996.
JOHANSEN, S. M. B. et al. Prediction of sensory properties of low-fat yoghurt and cream cheese from surface images. Food Quality and Preference, v. 19, p. 232-246, 2008. http://dx.doi.org/10.1016/j.foodqual.2007.03.006
KAROUI, R. et al. Prediction of sensory attributes of European Emmental cheese using near-infrared spectroscopy: A feasibility study. Food Chemistry, v. 101, p. 1121-1129, 2006. http://dx.doi.org/10.1016/j.foodchem.2006.03.012
KRZANOWSKI, W. Selection of variables, and assessment of their performance, in mixed-variable discriminant analysis. Computational Statistics and Data Analysis, v. 19, p. 419-431, 1995. http://dx.doi.org/10.1016/0167-9473(94)00011-7
KREUTZMANN, S. et al. Prediction of sensory quality in raw carrots (Daucus carota L.) using multi-block LS-ParPLS. Food Quality and Preference, v. 19, p. 609-617, 2008. http://dx.doi.org/10.1016/j.foodqual.2008.03.007
LEDAUPHIN, S.; HANAFI, M.; QANNARI, E. M. Assessment of the agreement among the subjects in fixed vocabulary profiling. Food Quality and Preference, v. 17, p. 277-280, 2006. http://dx.doi.org/10.1016/j.foodqual.2005.03.017
LEDAUPHIN, S.; POMMERET, D.; QANNARI, M. Application of hidden Markov model to products shelf lives. Food Quality and Preference, v.19, p.156-161, 2008. http://dx.doi.org/10.1016/j.foodqual.2007.04.006
MEILGAARD, M. C.; CARR, B. T.; CIVILLE, G. V. Sensory Evaluation Techniques. 4. ed. Boca Ratón: CRC Press, 1999. http://dx.doi.org/10.1201/9781439832271
MINGOTI, S. A. Análise de dados através de métodos de estatística multivariada: uma abordagem aplicada. Belo Horizonte: UFMG, 2005.
PERON, L. Statistical analysis of sensory profiling data: data reductionand generalised Procrustes analysis. Food Quality and Preference, v.11, p.155-157, 2000. http://dx.doi.org/10.1016/S0950-3293(99)00070-1
PIGGOTT, J. R.; SIMPSON, S. J.; WILLIAMS, S. A. R. Sensory analysis. International Journal of Food Science and Technology, v. 33, p. 7-18, 1998. http://dx.doi.org/10.1046/j.1365-2621.1998.00154.x
RENCHER, A. C. Methods of Multivariate Analysis. New York: Wiley, 1995.
SAHMER, K.; VIGNEAU, E.; QANNARI, E. M. A cluster approach to analyze preference data: choice of the number of clusters. Food Quality and Preference, v.17, p.257-265, 2006. http://dx.doi.org/10.1016/j.foodqual.2005.03.007
SAHMER, K.; QANNARI, E. M. Procedures for the selection of a subset of attributes in sensory profiling. Food Quality and Preference, v. 19, p. 141-145, 2008. http://dx.doi.org/10.1016/j.foodqual.2007.03.007
SEBER, G. A. F. Multivariate observations. New York: Wiley, 1984. http://dx.doi.org/10.1002/9780470316641
STONE, H.; SIDEL, J. Sensory Evaluation Practices. 2. ed. San Diego: Academic Press, 1992.
STONE, H. et al. Sensory Evaluation by Quantitative Descriptive Analysis. Food Technology, v. 28, n. 1, p. 24-34, 1974.
SUEYOSHI, T.; GOTO, M. Methodological comparison between DEA (data envelopment analysis) and DEA-DA (discriminant analysis) from the perspective of bankruptcy assessment. European Journal of Operational Research, v. 199, n. 2, p. 561-575, 2009. http://dx.doi.org/10.1016/j.ejor.2008.11.030
WESTADA, F. et al. Variable selection in PCA in sensory descriptive and consumer data. Food Quality and Preference, v. 14, p. 463-472, 2003. http://dx.doi.org/10.1016/S0950-3293(03)00015-6
ZANINE, A. M. et al. Evaluation of the grass-tanzania ("Panicum maximum") using multivariate analyses. Revista Brasileira de Saúde e Produção Animal, v. 9, p. 179-189, 2008.
ANZANELLO, M. J. Seleção de variáveis com vistas à classificação de bateladas de produção em duas classes. Gestão & Produção, v. 16, p. 526-533, 2009.
ANZANELLO, M. J.; ALBIN, S.; CHAOVALITWONGSE, W. A., Selecting the best variables for classifying production batches into two quality levels. Chemometrics and Intelligent Laboratory Systems, v. 97, p. 111-117, 2009. http://dx.doi.org/10.1016/j.chemolab.2009.03.004
CARBONELL, L.; IZQUIERDO, L.; CARBONELL, I. Sensory analysis of Spanish mandarin juices. Selection of attributes and panel performance. Food Quality and Preference, v. 18, p. 329-341, 2007. http://dx.doi.org/10.1016/j.foodqual.2006.02.008
CARBONELL, L. et al. Segmentation of food consumers according to their correlations with sensory attributes projected on preference spaces. Food Quality and Preference, v. 19, p. 71-78, 2008. http://dx.doi.org/10.1016/j.foodqual.2007.06.006
DIJKSTERHUIS, G.; FRØST, M. B.; BYRNE, D. V. Selection of a subset of variables: minimisation of Procrustes loss between a subset and the full set. Food Quality and Preference, v. 13, p. 89-97, 2002. http://dx.doi.org/10.1016/S0950-3293(01)00065-9
DUDA, R.; HART, P.; STORK, D. Pattern Classification. 2. ed. New York: Wiley-Interscience, 2001.
DUTCOSKY, S. D. Análise Sensorial de Alimentos. Curitiba: Champagnat, 1996.
ESTEBAN-DÍEZ, I.; GONZÁLEZ-SÁIZ, J. M.; PIZARRO, C. Prediction of sensory properties of espresso from roasted coffee samples by near-infrared spectroscopy. Analytica Chimica Acta, v. 525, p. 171-182, 2004. http://dx.doi.org/10.1016/j.aca.2004.08.057
FOGLIATTO, F. S.; ALBIN, S. L. A hierarchical method for evaluating products with quantitative and sensory characteristics. IIE Transactions, v. 33, p. 1081-1092, 2001. http://dx.doi.org/10.1080/07408170108936898
GRANITTO, P. M. et al. Modern data mining tools in descriptive sensory analysis: A case study with a Random forest approach. Food Quality and Preference, v. 18, p. 681-689, 2007. http://dx.doi.org/10.1016/j.foodqual.2006.11.001
HAIR, J. F. et al. Análise multivariada de dados. New York: Bookman, 2006.
HEENAN, S. P. et al. Characterization of fresh bread flavor: Relationships between sensory characteristics and volatile composition. Food Chemistry, v. 116, p. 249-257, 2009. http://dx.doi.org/10.1016/j.foodchem.2009.02.042
LAWLESS, H. T.; HEYMANN, H. Sensory Evaluation of Food: Principles and Practices. New York: Chapman & Hall, 1998.
JACKSON, J. E. Principal Component and factor Analysis: Part I - Principal Components. Journal of Quality Technology, v. 12, p. 201-213, 1980.
JACKSON, J. E. Principal Component and factor Analysis: Part II - Additional Topics Related to Principal Components. Journal of Quality Technology, v. 13, p. 46-58, 1981.
JOBSON, J. D. Applied multivariate data analysis. New York: Springer-Verlag, 1996.
JOHANSEN, S. M. B. et al. Prediction of sensory properties of low-fat yoghurt and cream cheese from surface images. Food Quality and Preference, v. 19, p. 232-246, 2008. http://dx.doi.org/10.1016/j.foodqual.2007.03.006
KAROUI, R. et al. Prediction of sensory attributes of European Emmental cheese using near-infrared spectroscopy: A feasibility study. Food Chemistry, v. 101, p. 1121-1129, 2006. http://dx.doi.org/10.1016/j.foodchem.2006.03.012
KRZANOWSKI, W. Selection of variables, and assessment of their performance, in mixed-variable discriminant analysis. Computational Statistics and Data Analysis, v. 19, p. 419-431, 1995. http://dx.doi.org/10.1016/0167-9473(94)00011-7
KREUTZMANN, S. et al. Prediction of sensory quality in raw carrots (Daucus carota L.) using multi-block LS-ParPLS. Food Quality and Preference, v. 19, p. 609-617, 2008. http://dx.doi.org/10.1016/j.foodqual.2008.03.007
LEDAUPHIN, S.; HANAFI, M.; QANNARI, E. M. Assessment of the agreement among the subjects in fixed vocabulary profiling. Food Quality and Preference, v. 17, p. 277-280, 2006. http://dx.doi.org/10.1016/j.foodqual.2005.03.017
LEDAUPHIN, S.; POMMERET, D.; QANNARI, M. Application of hidden Markov model to products shelf lives. Food Quality and Preference, v.19, p.156-161, 2008. http://dx.doi.org/10.1016/j.foodqual.2007.04.006
MEILGAARD, M. C.; CARR, B. T.; CIVILLE, G. V. Sensory Evaluation Techniques. 4. ed. Boca Ratón: CRC Press, 1999. http://dx.doi.org/10.1201/9781439832271
MINGOTI, S. A. Análise de dados através de métodos de estatística multivariada: uma abordagem aplicada. Belo Horizonte: UFMG, 2005.
PERON, L. Statistical analysis of sensory profiling data: data reductionand generalised Procrustes analysis. Food Quality and Preference, v.11, p.155-157, 2000. http://dx.doi.org/10.1016/S0950-3293(99)00070-1
PIGGOTT, J. R.; SIMPSON, S. J.; WILLIAMS, S. A. R. Sensory analysis. International Journal of Food Science and Technology, v. 33, p. 7-18, 1998. http://dx.doi.org/10.1046/j.1365-2621.1998.00154.x
RENCHER, A. C. Methods of Multivariate Analysis. New York: Wiley, 1995.
SAHMER, K.; VIGNEAU, E.; QANNARI, E. M. A cluster approach to analyze preference data: choice of the number of clusters. Food Quality and Preference, v.17, p.257-265, 2006. http://dx.doi.org/10.1016/j.foodqual.2005.03.007
SAHMER, K.; QANNARI, E. M. Procedures for the selection of a subset of attributes in sensory profiling. Food Quality and Preference, v. 19, p. 141-145, 2008. http://dx.doi.org/10.1016/j.foodqual.2007.03.007
SEBER, G. A. F. Multivariate observations. New York: Wiley, 1984. http://dx.doi.org/10.1002/9780470316641
STONE, H.; SIDEL, J. Sensory Evaluation Practices. 2. ed. San Diego: Academic Press, 1992.
STONE, H. et al. Sensory Evaluation by Quantitative Descriptive Analysis. Food Technology, v. 28, n. 1, p. 24-34, 1974.
SUEYOSHI, T.; GOTO, M. Methodological comparison between DEA (data envelopment analysis) and DEA-DA (discriminant analysis) from the perspective of bankruptcy assessment. European Journal of Operational Research, v. 199, n. 2, p. 561-575, 2009. http://dx.doi.org/10.1016/j.ejor.2008.11.030
WESTADA, F. et al. Variable selection in PCA in sensory descriptive and consumer data. Food Quality and Preference, v. 14, p. 463-472, 2003. http://dx.doi.org/10.1016/S0950-3293(03)00015-6
ZANINE, A. M. et al. Evaluation of the grass-tanzania ("Panicum maximum") using multivariate analyses. Revista Brasileira de Saúde e Produção Animal, v. 9, p. 179-189, 2008.
Facebook
Google+
Twitter
LinkedIn
Mendeley
StumbleUpon
CiteULike
Reddit
Email