Evaluation of rapid descriptive sensory methods with different panels in the characteristics variations of beers packaged in distinct materials

Authors

DOI:

https://doi.org/10.33448/rsd-v9i9.6137

Keywords:

Descriptive analysis; Pivot profile; Projective mapping.

Abstract

Two new rapid descriptive sensory evaluation methods have been gaining ground in the field sensory evaluation. The Projective Mapping method uses similarities and dissimilarities as a criterion, while Pivot Profile, uses reference criteria. This research aimed to assess panels with 12 and 24 judges, comparing its reproducibility, and evaluate if a non trained panel with a smaller numbers of judges is sufficient for results reliability. Samples of Pilsen beers in different packages were distributed, as well as a reference sample, with different sensorial characteristics. It was possible to observe a slight discrepancy between the results obtained in each of the applied tests. We observed a need for short-term training before the application of the test, aiming for better use of the descriptive terms by the judges. Also, the number of judges influenced the obtained results, being the panels of 24, in both tests, the ones that best described the indicated characteristics.

References

Alcantara, M. D., & Freitas-Sá, D. D. G. C. (2018). Metodologias sensoriais descritivas mais rápidas e versáteis, uma atualidade na ciência sensorial. Brazilian Journal Food Technology, 21, 1-12. https://doi.org/10.1590/1981-6723.17916.

Barnett, A., Velasco, C., & Spence, C. (2016). Bottled vs. canned beer: Do they really taste different?. Beverages, 2(4), 25. https://doi.org/10.3390/beverages2040025.

Barry, S., Muggah, E. M., McSweeney, M. B., & Walker, S. (2017). A preliminary investigation into differences in hops’ aroma attributes. International Journal of Food Science & Technology, 53(3), 804-811. https://doi.org/10.1111/ijfs.13656.

Carvalho, L. C., Mafaldo, I. M., Rockenbach, I. I., Oliveira, K. K. G., Lima, L. G. A. C...& Mishina, R. A. G. (2020). Chemical and sensory profile of craft beer produced using algaroba (Prosopis juliflora) as malt adjunct. Research, Society and Development, 9 (8), e769986041. http://dx.doi.org/10.33448/rsd-v9i8.6041.

Carrilho, E., Varela, P., & Fiszman, S. (2012). Packaging information as a modulator of consumers’ perception of enriched and reduced-calorie biscuits in tasting and non-tasting tests. Food Quality and Preference, 25(2), 105-115. https://doi.org/10.1016/j.foodqual.2012.02.005.

Chollet, S., Lelièvre, M., Abdi, H., & Valentin, D. (2011). Sort and beer: Everything you wanted to know about the sorting task but did not dare to ask. Food quality and preference, 22(6), 507-520. https://doi.org/10.1016/j.foodqual.2011.02.004.

Deneulin, P., Reverdy, C., Rébénaque, P., Danthe, E., & Mulhauser, B. (2018). Evaluation of the Pivot Profile©, a new method to characterize a large variety of a single product: Case study on honeys from around the world. Food research international, 106, 29-37. https://doi.org/10.1016/j.foodres.2017.12.044.

Drake, M. A. (2007). Invited review: Sensory analysis of dairy foods. Journal of dairy science, 90(11), 4925-4937. https://doi.org/10.3168/jds.2007-0332.

Esmerino, E. A., Tavares Filho, E. R., Carr, B. T., Ferraz, J. P., Silva, H. L., Pinto, L. P., & Bolini, H. M. (2017). Consumer-based product characterization using Pivot Profile, Projective Mapping and Check-all-that-apply (CATA): A comparative case with Greek yogurt samples. Food research international, 99, 375-384. https://doi.org/10.1016/j.foodres.2017.06.001.

Fleming, E. E., Ziegler, G. R., & Hayes, J. E. (2015). Check-all-that-apply (CATA), sorting, and polarized sensory positioning (PSP) with astringent stimuli. Food quality and preference, 45, 41-49. https://doi.org/10.1016/j.foodqual.2015.05.004.

Fonseca, F. G., Esmerino, E. A., Filho, E. R., Ferraz, J. P., Cruz, A. G., & Bolini, H. M. (2016). Novel and successful free comments method for sensory characterization of chocolate ice cream: A comparative study between pivot profile and comment analysis. Journal of Dairy Science, 99(5), 3408–3420. https://doi.org/10.3168/jds.2015-9982.

Heymann, H., Machado, B., Torri, L., & Robinson, A.,L. (2012). How many judges should one use for sensory descriptive analysis?. Journal of sensory studies, 27(2), 111-122. https://doi.org/10.1111/j.1745-459X.2012.00373.x.

Hopfer H., & Heymann, H.,A (2013). Summary of projective mapping observations–The effect of replicates and shape, and individual performance measurements. Food Quality and Preference, 28(1),164-181. https://doi.org/10.1016/j.foodqual.2012.08.017.

Ivušić, F., Soldo Gjeldum, M., Nemet, Z., Gracin, L., & Marić, V. (2006). Aluminium and aroma compound concentration in beer during storage at different temperatures. Food Technology and Biotechnology, 44(4), 499-505.

Jantzi, H., Hayward, L., Barton, A., Richardson, C. D., & McSweeney, M. B. (2020). Investigating the effect of extrinsic cues on consumers' evaluation of red wine using a projective mapping task. Journal of Sensory Studies. https://doi.org/10.1111/joss.12568.

Lê, S., Josse, J., & Husson, F. (2007). FactoMineR: an R package for multivariate analysis. Journal of statistical software, 25(1), 1-18.

Lelièvre-Desmas, M., Valentin, D., & Chollet, S. (2017). Pivot profile method: What is the influence of the pivot and product space?. Food quality and preference, 61, 6-14. https://doi.org/10.1016/j.foodqual.2017.05.002.

Mora, M., Matos, A. D., Fernández-Ruiz, V., Briz, T., & Chaya, C. (2020). Comparison of methods to develop an emotional lexicon of wine: Conventional vs Rapid-method approach. Food Quality and Preference, 103920. https://doi.org/10.1016/j.foodqual.2020.103920.

Morais, E. C., Cruz, A. G., Faria, J. A. F., & Bolini, H. M. A. (2014). Prebiotic gluten-free bread: Sensory profiling and drivers of liking. LWT-Food Science and Technology, 55(1), 248-254. https://doi.org/10.1016/j.lwt.2013.07.014.

Murray, J. M., & Delahunty, C. M. (2000.) Mapping consumer preference for the sensory and packaging attributes of cheddar cheese. Food Quality and Preference, 11(5), 419-435. https://doi.org/10.1016/S0950-3293(00)00017-3.

Pagès, J. (2005) Collection and analysis of perceived product inter-distances using multiple factor analysis: Application to the study of 10 white wines from the Loire Valley. Food Quality and Preference, 16(7), 642–649. https://doi.org/10.1016/j.foodqual.2005.01.006.

Pearson, W., Schmidtke, L., Francis, I. L., & Blackman, J. W. (2020). An investigation of the Pivot© Profile sensory analysis method using wine experts: Comparison with descriptive analysis and results from two expert panels. Food Quality and Preference, 83, 103858. https://doi.org/10.1016/j.foodqual.2019.103858.

Pereira, A. S., et al. (2018). Methodology of cientific research. [e-Book]. Santa Maria City. UAB / NTE / UFSM Editors. Retrieved from https://repositorio.ufsm.br/bitstream/handle/1/15824/Lic_Computacao_Metodologia-Pesquisa-Cientifica.pdf?sequence=1.

Pérez‐Navarro J., Izquierdo‐Cañas P. M., Mena‐Morales A., Martínez‐Gascueña, J., Chacón‐Vozmediano, J. L., García‐Romero, E., & Hermosín‐Gutiérrez, I. (2018). First chemical and sensory characterization of Moribel and Tinto Fragoso wines using HPLC‐DAD‐ESI‐MS/MS, GC‐MS, and Napping® techniques: comparison with Tempranillo. Journal of the Science of Food and Agriculture, 99(5), 2108-2123. https://doi.org/10.1002/jsfa.9403.

Queiroga de Paula, I., & Ferreira, E. B. (2019). Análise sensorial de alimentos: uma comparação de testes para a seleção de potenciais provadores. Caderno de Ciências Agrárias, 11, 1-8.

R Development Core Team (2007). R: A language and environment for computing. Vienna: R Foundation for Statistical Computing. ISBN 3-900051-07-0.

Reinbach, H. C., Giacalone D., Ribeiro L. M., Bredie W. L & Frøst M. B. (2014). Comparison of three sensory profiling methods based on consumer perception: CATA, CATA with intensity and Napping®. Food Quality and Preference, 32, 160-166. https://doi.org/10.1016/j.foodqual.2013.02.004.

Thuillier B., Valentin D., Marchal, R., & Dacremont, C. (2015). Pivot© profile: A new descriptive method based on free description. Food Quality and Preference, 42, 66-77. https://doi.org/10.1016/j.foodqual.2015.01.012.

Torri, L., Dinnella, C., Recchia, A., Naes, T., Tuorila, H., & Monteleone, E. (2013). Projective Mapping for interpreting wine aroma differences as perceived by naïve and experienced assessors. Food Quality and Preference, 29(1), 6-15. https://doi.org/10.1016/j.foodqual.2013.01.006.

Valentin, D., Chollet, S., Lelièvre, M., & Abdi, H. (2012). Quick and dirty but still pretty good: A review of new descriptive methods in food science. International Journal of Food Science & Technology, 47(8),1563-1578. https://doi.org/10.1111/j.1365-2621.2012.03022.x.

Varela, P., & Ares, G. (2012). Sensory profiling, the blurred line between sensory and consumer science. A review of novel methods for product characterization. Food Research International, 48(2), 893–908. https://doi.org/10.1016/j.foodres.2012.06.037.

Veinand, B., Godefroy, C., Adam, C., & Delarue, J. (2011). Highlight of important product characteristics for consumers. Comparison of three sensory descriptive methods performed by consumers. Food Quality and Preference, 22(5), 474-485. https://doi.org/10.1016/j.foodqual.2011.02.011.

Vidal, L., Cadena, R. S., Antúnez, L., Giménez, A., Varela, P., & Ares, G. (2014). Stability of sample configurations from projective mapping: How many consumers are necessary?. Food Quality and Preference, 34, 79-87. http://dx.doi.org/10.1016/j.foodqual.2013.12.006.

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Published

09/08/2020

How to Cite

FRONZA, P.; SILVA, A. R. C. S.; LEÓN, M. P.; VILAÇA, A. C.; GUIRLANDA, C. P.; DUTRA, V. L. M.; FANTE, C. A. Evaluation of rapid descriptive sensory methods with different panels in the characteristics variations of beers packaged in distinct materials. Research, Society and Development, [S. l.], v. 9, n. 9, p. e08996137, 2020. DOI: 10.33448/rsd-v9i9.6137. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/6137. Acesso em: 16 nov. 2024.

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Section

Agrarian and Biological Sciences