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Título : Optimization of texture profile analysis parameters for commercial guava preserve.
Autor : Vieira, Mariele Antunes
Schiassi, Maria Cecília Evangelista Vasconcelos
Dias, Ana Clara Costa
Curi, Paula Nogueira
Pereira, Patrícia Aparecida Pimenta
Carneiro, João de Deus Souza
Borges, Soraia Vilela
Queiroz, Fabiana
Palabras clave : Design central composite rotational
Food quality
Testing machines
Fecha de publicación : 2021
Citación : VIEIRA, M. A. et al. Optimization of texture profile analysis parameters for commercial guava preserve. Revista Ceres, Viçosa, v. 68, n. 6, p. 530-538, nov./dez. 2021. Disponível em: <https://www.scielo.br/j/rceres/a/XtQ4GHG3yf6qh4D5nx6DrQP/>. Acesso em: 11 out. 2022.
Resumen : Motivated by the lack of studies that standardize and optimize the parameters of texture tests, this study aimed to determine the operating conditions for TPA to maximize the discrimination among samples of fruit preserves. The texture of the commercial guava preserves was evaluated using a texturometer. The Design Central Composite Rotational (DCCR) method was applied with four independent variables: speed test, sample volume, time between compression cycles and compression percentage. Only the compression percentage and test speed were significantly influenced by the texture parameters evaluated. The optimum operating region of TPA to better discriminate differences in texture parameters depended on the variable to be optimized, and for adhesiveness a compression of 75% and a compression speed of 0.23 mm·s are recommended. To detect differences among the samples for the parameters of cohesiveness, gumminess and resilience, the use of 15% compression and 2.59 mm·s speed is suggested. In both cases, one must employ the shortest time between two cycles and use a smaller sample size to save both the time of analysis and of the sample, respectively. For the parameters of hardness, elasticity and chewiness, optimal regions were not identified.
URI : http://www.repositorio.ufop.br/jspui/handle/123456789/16255
metadata.dc.identifier.doi: https://doi.org/10.1590/0034-737X202168060004
ISSN : 2177-3491
metadata.dc.rights.license: This is an open access article under the CC BY license Creative Commons. Fonte: o PDF do artigo.
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