Optimization of texture profile analysis parameters for commercial guava preserve

Detalhes bibliográficos
Autor(a) principal: Vieira,Mariele Antunes
Data de Publicação: 2021
Outros Autores: 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
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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spelling 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
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