Optimization of texture profile analysis parameters for commercial guava preserve
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Ceres |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0034-737X2021000600530 |
Resumo: | ABSTRACT 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. |
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Optimization of texture profile analysis parameters for commercial guava preservedesign central composite rotationalfood qualitytesting machinesABSTRACT 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.Universidade Federal de Viçosa2021-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0034-737X2021000600530Revista Ceres v.68 n.6 2021reponame:Revista Ceresinstname:Universidade Federal de Viçosa (UFV)instacron:UFV10.1590/0034-737x202168060004info:eu-repo/semantics/openAccessVieira,Mariele AntunesSchiass,Maria Cecília Evangelista VasconcelosDias,Ana Clara CostaCuri,Paula NogueiraPereira,Patrícia Aparecida PimentaCarneiro,João De Deus SouzaBorges,Soraia VilelaQueiroz,Fabianaeng2021-11-30T00:00:00ZRevista |
dc.title.none.fl_str_mv |
Optimization of texture profile analysis parameters for commercial guava preserve |
title |
Optimization of texture profile analysis parameters for commercial guava preserve |
spellingShingle |
Optimization of texture profile analysis parameters for commercial guava preserve Vieira,Mariele Antunes design central composite rotational food quality testing machines |
title_short |
Optimization of texture profile analysis parameters for commercial guava preserve |
title_full |
Optimization of texture profile analysis parameters for commercial guava preserve |
title_fullStr |
Optimization of texture profile analysis parameters for commercial guava preserve |
title_full_unstemmed |
Optimization of texture profile analysis parameters for commercial guava preserve |
title_sort |
Optimization of texture profile analysis parameters for commercial guava preserve |
author |
Vieira,Mariele Antunes |
author_facet |
Vieira,Mariele Antunes Schiass,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 |
author_role |
author |
author2 |
Schiass,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 |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Vieira,Mariele Antunes Schiass,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 |
dc.subject.por.fl_str_mv |
design central composite rotational food quality testing machines |
topic |
design central composite rotational food quality testing machines |
dc.description.none.fl_txt_mv |
ABSTRACT 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. |
description |
ABSTRACT 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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0034-737X2021000600530 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0034-737X2021000600530 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0034-737x202168060004 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Universidade Federal de Viçosa |
publisher.none.fl_str_mv |
Universidade Federal de Viçosa |
dc.source.none.fl_str_mv |
Revista Ceres v.68 n.6 2021 reponame:Revista Ceres instname:Universidade Federal de Viçosa (UFV) instacron:UFV |
instname_str |
Universidade Federal de Viçosa (UFV) |
instacron_str |
UFV |
institution |
UFV |
reponame_str |
Revista Ceres |
collection |
Revista Ceres |
repository.name.fl_str_mv |
|
repository.mail.fl_str_mv |
|
_version_ |
1728006784199688192 |