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Definition of network types – Prediction of dough mechanical behaviour under shear by gluten microstructure

This study defines network types of wheat gluten to describe spatial arrangements of gluten networks in relation to dough mechanical behaviour. To achieve a high variety in gluten arrangements, ten specific and unspecific gluten-modifying agents in increasing concentrations were added to wheat dough...

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Autores principales: Lucas, Isabelle, Petermeier, Hannes, Becker, Thomas, Jekle, Mario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6423116/
https://www.ncbi.nlm.nih.gov/pubmed/30886245
http://dx.doi.org/10.1038/s41598-019-41072-w
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author Lucas, Isabelle
Petermeier, Hannes
Becker, Thomas
Jekle, Mario
author_facet Lucas, Isabelle
Petermeier, Hannes
Becker, Thomas
Jekle, Mario
author_sort Lucas, Isabelle
collection PubMed
description This study defines network types of wheat gluten to describe spatial arrangements of gluten networks in relation to dough mechanical behaviour. To achieve a high variety in gluten arrangements, ten specific and unspecific gluten-modifying agents in increasing concentrations were added to wheat dough. Gluten microstructure was visualized by confocal laser scanning microscopy and quantified by protein network analysis. Dough rheological behaviour was determined by both oscillatory and creep-recovery tests. Based on correlation matrices and principal component analysis, six different network types were identified and associated to their rheological characteristics: a cleaved (low viscous), rigid (highly viscous), spread (viscoelastic), strengthened (viscoelastic), particulate and dense (highly viscous) or particulate and loose (low viscous) network. Furthermore, rheological dough properties of specifically gluten-modified samples were predicted with five microstructural gluten attributes (lacunarity, branching rate, end-point rate, protein width, average protein length) and assigned properly by the obtained partial least square model with an accuracy up to 90% (e.g., R(2)Y = 0.84 for G*, 0.85 for tanδ, 0.90 for J(max)). As a result, rheological properties of wheat doughs were predicted from microstructural investigations. This novel, quantitative definition of the relation between structure and mechanical behaviour can be used for developments of new wheat products with targeted properties.
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spelling pubmed-64231162019-03-26 Definition of network types – Prediction of dough mechanical behaviour under shear by gluten microstructure Lucas, Isabelle Petermeier, Hannes Becker, Thomas Jekle, Mario Sci Rep Article This study defines network types of wheat gluten to describe spatial arrangements of gluten networks in relation to dough mechanical behaviour. To achieve a high variety in gluten arrangements, ten specific and unspecific gluten-modifying agents in increasing concentrations were added to wheat dough. Gluten microstructure was visualized by confocal laser scanning microscopy and quantified by protein network analysis. Dough rheological behaviour was determined by both oscillatory and creep-recovery tests. Based on correlation matrices and principal component analysis, six different network types were identified and associated to their rheological characteristics: a cleaved (low viscous), rigid (highly viscous), spread (viscoelastic), strengthened (viscoelastic), particulate and dense (highly viscous) or particulate and loose (low viscous) network. Furthermore, rheological dough properties of specifically gluten-modified samples were predicted with five microstructural gluten attributes (lacunarity, branching rate, end-point rate, protein width, average protein length) and assigned properly by the obtained partial least square model with an accuracy up to 90% (e.g., R(2)Y = 0.84 for G*, 0.85 for tanδ, 0.90 for J(max)). As a result, rheological properties of wheat doughs were predicted from microstructural investigations. This novel, quantitative definition of the relation between structure and mechanical behaviour can be used for developments of new wheat products with targeted properties. Nature Publishing Group UK 2019-03-18 /pmc/articles/PMC6423116/ /pubmed/30886245 http://dx.doi.org/10.1038/s41598-019-41072-w Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Lucas, Isabelle
Petermeier, Hannes
Becker, Thomas
Jekle, Mario
Definition of network types – Prediction of dough mechanical behaviour under shear by gluten microstructure
title Definition of network types – Prediction of dough mechanical behaviour under shear by gluten microstructure
title_full Definition of network types – Prediction of dough mechanical behaviour under shear by gluten microstructure
title_fullStr Definition of network types – Prediction of dough mechanical behaviour under shear by gluten microstructure
title_full_unstemmed Definition of network types – Prediction of dough mechanical behaviour under shear by gluten microstructure
title_short Definition of network types – Prediction of dough mechanical behaviour under shear by gluten microstructure
title_sort definition of network types – prediction of dough mechanical behaviour under shear by gluten microstructure
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6423116/
https://www.ncbi.nlm.nih.gov/pubmed/30886245
http://dx.doi.org/10.1038/s41598-019-41072-w
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