Portable solutions for quality control of beers: a review

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DOI:

https://doi.org/10.5327/fst.00051%20

Palavras-chave:

portable, technology, sensory, beer, quality

Resumo

Brazil is one of the top five market leaders in beer production. The quantity of establishments legally registered is expected to increase in the coming years. The quality of beer is measured by a complex set of sensory characteristics that include appearance, aroma, taste, and texture. It is also composed of more than 800 chemical compounds originating from different raw materials. The evaluation of the product quality of this process is extremely necessary in order to guarantee customer security and satisfaction. Although human and chemical analyses can be considered complex, there is a tendency to use artificial intelligence technology for precise and cheaper evaluation. Sensory training technologies for evaluators and electronic nose technologies have the purpose of facilitating the recognition of on-and-off characteristics of beer. There were found and discussed one review article, four patents, seven commercial products concerning aroma sensory training kits, and the top 10 technology areas screened with the Orbit® platform. Eight research articles were also highlighted about electronic nose technology, one commercially available, and the top 10 technology areas based on Orbit® platform data. The advent of such technologies represents a step forward in improving quality assurance, but electronic nose technologies do not replace human evaluators yet, because human recognition is a decision factor in releasing a product to market shelves.

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Referências

Asih, N. E., Ramadhanty, K. P., Ramandias, J., Azkarama, F., & Sunarharum, W. B. (2021). Lexicon, sensory wheel and kit as sensory communication tools: a review. IOP Conference Series: Earth and Environmental Science. IOP Publishing. https://doi.org/10.1088/1755-1315/924/1/012027

Associação Brasileira de Normas Técnicas (ABNT) (2022). Análise sensorial: Metodologia. Diretrizes para a avaliação do desempenho de um painel sensorial descritivo quantitativo. ABNT. Retrieved from https://www.abnt.org.br/

Bastgen, N., Ginzel, M., & Titze, J. (2019). Precision of a small brew house by determining the repeatability of different brews to guarantee the product stability of the beer. Beverages, 5(4), 67. https://doi.org/10.3390/beverages5040067

Brasil (1996). Lei nº 9.279, de 14 de maio de 1996. Regula direitos e obrigações relativos à propriedade industrial. Retrieved from https://www.planalto.gov.br/ccivil_03/leis/l9279.htm

Brasil (2021). Ministério da Agricultura, Pecuária e Abastecimento. Anuário da cerveja. Ministério da Agricultura, Pecuária e Abastecimento.

Brewers Association (ed.) (2014). Best Practices Guide to Quality Craft Beer: Delivering Optimal Flavour to the Consumer. Brewers Association. Retrieved from https://www.brewersassociation.org/educational-publications/best-practices-guide-to-quality-craft-beer/

Ghesti, G., Carvalho, I., Carmo, T., & Suarez, P. A. Z. (2023). A newer source of microorganism to produce Catharina Sour beers. Food Science and Technology, 43, e102022. https://doi.org/10.1590/fst.102022

Gomes, F. de O., Guimarães, B. P., Ceola, D., & Ghesti, G. (2022). Advances in dry hopping for industrial brewing: A review. Food Science and Technology, 42, e60620. https://doi.org/10.1590/fst.60620

González-Martín, I., Pérez-Pavón, J. L., González-Pérez, C.; Hernández-Méndez, J., & Álvarez-García, N. (2000). Differentiation of products derived from Iberian breed swine by electronic olfactometry (electronic nose). Analytica Chimica Acta, 424(2), 279-287. https://doi.org/10.1016/S0003-2670(00)01106-5

Gonzalez Viejo, C., Fuentes, S. (2020). Low-cost methods to assess beer quality using artificial intelligence involving robotics, an electronic nose, and machine learning. Fermentation, 6(4), 104. https://doi.org/10.3390/fermentation6040104

Habschied, K., Krstanović, V., & Mastanjević, K. (2022). Beer Quality Evaluation: A Sensory Aspect. Beverages, 8(1), 15. https://doi.org/10.3390/beverages8010015

Lamas, F. C., Mello, L. R., & Ghesti, G. F. (2022). Tratamento do segredo industrial na transferência de tecnologia dos produtos estratégicos de defesa: questionamentos a partir da lei de acesso à informação. Cadernos de Prospecção, 15(3), 792-805. https://doi.org/10.9771/cp.v15i3.46143

Muller, C., Neves, L. E., Gomes, L., Guimarães, M., & Ghesti, G. (2019). Processes for alcohol-free beer production: a review. Food Science and Technology, 40(2), 273-281. https://doi.org/10.1590/fst.32318

Nunes, C. A., Ribeiro, M. N., Carvalho, T. C. L., Ferreira, D. D., Oliveira, L. L., & Pinheiro, A. C. M. (2023). Artificial intelligence in sensory and consumer studies of food products. Current Opinion in Food Science, 50, 101002. https://doi.org/10.1016/j.cofs.2023.101002

Reitenbach, A. F. (2016). Desenvolvimento de nariz eletrônico para compostos voláteis da cerveja. Doctoral Thesis, Universidade Federal de Santa Catarina.

Santos, J. P., Lozano, J., & Aleixandre, M. (2017). Electronic noses applications in beer technology. Brewing Technology, 177. https://doi.org/10.5772/intechopen.68822

Schmillen, A. (2009). The beer aroma wheel. Brewing Science, 62, 26.

Science of Beer Institute. Aroma Sensory Training®: Guide Manual. Science of Beer Institute. https://doi.org/10.29327/5194321

Silvello, G. C., Bortoletto, A. M., & Alcarde, A. R. (2020). The barrel aged beer wheel: a tool for sensory assessment. Journal of the Institute of Brewing, 126(4), 382-393. https://doi.org/10.1002/jib.626

Statista (2023). Consumer Market Insights, Alcoholic Drinks. Beer, South America. Statista. Retrieved from https://www.statista.com/outlook/cmo/alcoholic-drinks/beer/south-america#global-comparison

Voss, H. G. J., Mendes Júnior, J. J. A., Farinelli, M. E., & Stevan Jr., S. L. (2019). A prototype to detect the alcohol content of beers based on an electronic nose. Sensors, 19(11), 2646. https://doi.org/10.3390%2Fs19112646

Wang, A., Qiu, J., Cao, R., & Zhu, H. (2022). Application of intelligent sensory technology in the authentication of alcoholic beverages. Food Science and Technology, 42, e32622. https://doi.org/10.1590/fst.32622

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Publicado

2024-02-09

Como Citar

ORLANDI, A. C. de A. L., REITENBACH, A. F., CARMO, T. S., & GHESTI, G. F. (2024). Portable solutions for quality control of beers: a review. Food Science and Technology, 44. https://doi.org/10.5327/fst.00051

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Artigos de Revisão