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Dataset on viscosity and starch polymer properties to predict texture through modeling

Accurate classification tool for screening varieties with superior eating and cooking quality based on its pasting and starch structure properties is in demand to satisfy both consumers’ and farmers’ need. Here we showed the data related to the article entitled “Deploying viscosity and starch polyme...

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Detalles Bibliográficos
Autores principales: Buenafe, Reuben James Q., Kumanduri, Vasudev, Sreenivasulu, Nese
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8100065/
https://www.ncbi.nlm.nih.gov/pubmed/33997194
http://dx.doi.org/10.1016/j.dib.2021.107038
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author Buenafe, Reuben James Q.
Kumanduri, Vasudev
Sreenivasulu, Nese
author_facet Buenafe, Reuben James Q.
Kumanduri, Vasudev
Sreenivasulu, Nese
author_sort Buenafe, Reuben James Q.
collection PubMed
description Accurate classification tool for screening varieties with superior eating and cooking quality based on its pasting and starch structure properties is in demand to satisfy both consumers’ and farmers’ need. Here we showed the data related to the article entitled “Deploying viscosity and starch polymer properties to predict cooking and eating quality models: a novel breeding tool to predict texture” [1] which provides solution to this problem. The paper compiles all the pasting, starch structure, sensory and routine quality data of the rice sample used in the article into graphical form. It also shows how the data were processed and obtained.
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spelling pubmed-81000652021-05-13 Dataset on viscosity and starch polymer properties to predict texture through modeling Buenafe, Reuben James Q. Kumanduri, Vasudev Sreenivasulu, Nese Data Brief Data Article Accurate classification tool for screening varieties with superior eating and cooking quality based on its pasting and starch structure properties is in demand to satisfy both consumers’ and farmers’ need. Here we showed the data related to the article entitled “Deploying viscosity and starch polymer properties to predict cooking and eating quality models: a novel breeding tool to predict texture” [1] which provides solution to this problem. The paper compiles all the pasting, starch structure, sensory and routine quality data of the rice sample used in the article into graphical form. It also shows how the data were processed and obtained. Elsevier 2021-04-03 /pmc/articles/PMC8100065/ /pubmed/33997194 http://dx.doi.org/10.1016/j.dib.2021.107038 Text en © 2021 The Authors. Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Buenafe, Reuben James Q.
Kumanduri, Vasudev
Sreenivasulu, Nese
Dataset on viscosity and starch polymer properties to predict texture through modeling
title Dataset on viscosity and starch polymer properties to predict texture through modeling
title_full Dataset on viscosity and starch polymer properties to predict texture through modeling
title_fullStr Dataset on viscosity and starch polymer properties to predict texture through modeling
title_full_unstemmed Dataset on viscosity and starch polymer properties to predict texture through modeling
title_short Dataset on viscosity and starch polymer properties to predict texture through modeling
title_sort dataset on viscosity and starch polymer properties to predict texture through modeling
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8100065/
https://www.ncbi.nlm.nih.gov/pubmed/33997194
http://dx.doi.org/10.1016/j.dib.2021.107038
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