Dataset of Near-infrared spectroscopy measurement for amylose determination using PLS algorithms

In the dataset presented in this article, 168 rice samples comprising sixteen rice varieties (including Indica and Japonica sub species) from a Portuguese Rice Breeding Program obtained from three different sites along four seasons, and 11 standard rice varieties from International Rice Research Ins...

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Detalles Bibliográficos
Autores principales: Sampaio, P., Soares, A., Castanho, A., Almeida, A.S., Oliveira, J., Brites, C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712058/
https://www.ncbi.nlm.nih.gov/pubmed/29214199
http://dx.doi.org/10.1016/j.dib.2017.09.077
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author Sampaio, P.
Soares, A.
Castanho, A.
Almeida, A.S.
Oliveira, J.
Brites, C.
author_facet Sampaio, P.
Soares, A.
Castanho, A.
Almeida, A.S.
Oliveira, J.
Brites, C.
author_sort Sampaio, P.
collection PubMed
description In the dataset presented in this article, 168 rice samples comprising sixteen rice varieties (including Indica and Japonica sub species) from a Portuguese Rice Breeding Program obtained from three different sites along four seasons, and 11 standard rice varieties from International Rice Research Institute were characterised. The amylose concentration was evaluated based on iodine method, and the near infrared (NIR) spectra were determined. To assess the advantage of Near infrared spectroscopy, different rice varieties and specific algorithms based on Matlab software such as Standard Normal Variate (SNV), Multiple Scatter Calibration (MSC) and Savitzky-Golay filter were used for NIR spectra pre-processing.
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spelling pubmed-57120582017-12-06 Dataset of Near-infrared spectroscopy measurement for amylose determination using PLS algorithms Sampaio, P. Soares, A. Castanho, A. Almeida, A.S. Oliveira, J. Brites, C. Data Brief Article In the dataset presented in this article, 168 rice samples comprising sixteen rice varieties (including Indica and Japonica sub species) from a Portuguese Rice Breeding Program obtained from three different sites along four seasons, and 11 standard rice varieties from International Rice Research Institute were characterised. The amylose concentration was evaluated based on iodine method, and the near infrared (NIR) spectra were determined. To assess the advantage of Near infrared spectroscopy, different rice varieties and specific algorithms based on Matlab software such as Standard Normal Variate (SNV), Multiple Scatter Calibration (MSC) and Savitzky-Golay filter were used for NIR spectra pre-processing. Elsevier 2017-10-06 /pmc/articles/PMC5712058/ /pubmed/29214199 http://dx.doi.org/10.1016/j.dib.2017.09.077 Text en © 2017 Published by Elsevier Inc. http://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 Article
Sampaio, P.
Soares, A.
Castanho, A.
Almeida, A.S.
Oliveira, J.
Brites, C.
Dataset of Near-infrared spectroscopy measurement for amylose determination using PLS algorithms
title Dataset of Near-infrared spectroscopy measurement for amylose determination using PLS algorithms
title_full Dataset of Near-infrared spectroscopy measurement for amylose determination using PLS algorithms
title_fullStr Dataset of Near-infrared spectroscopy measurement for amylose determination using PLS algorithms
title_full_unstemmed Dataset of Near-infrared spectroscopy measurement for amylose determination using PLS algorithms
title_short Dataset of Near-infrared spectroscopy measurement for amylose determination using PLS algorithms
title_sort dataset of near-infrared spectroscopy measurement for amylose determination using pls algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712058/
https://www.ncbi.nlm.nih.gov/pubmed/29214199
http://dx.doi.org/10.1016/j.dib.2017.09.077
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