Performances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributes

The data presented in this article are related to the research article entitled “Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef” [1]. Partial least squares regression (PLSR) models were developed on Raman spectral data pre-trea...

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Autores principales: Zhao, Ming, Nian, Yingqun, Allen, Paul, Downey, Gerard, Kerry, Joseph P., O’Donnell, Colm P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6141602/
https://www.ncbi.nlm.nih.gov/pubmed/30246069
http://dx.doi.org/10.1016/j.dib.2018.04.056
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author Zhao, Ming
Nian, Yingqun
Allen, Paul
Downey, Gerard
Kerry, Joseph P.
O’Donnell, Colm P.
author_facet Zhao, Ming
Nian, Yingqun
Allen, Paul
Downey, Gerard
Kerry, Joseph P.
O’Donnell, Colm P.
author_sort Zhao, Ming
collection PubMed
description The data presented in this article are related to the research article entitled “Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef” [1]. Partial least squares regression (PLSR) models were developed on Raman spectral data pre-treated using Savitzky Golay (S.G.) derivation (with 2nd or 5th order polynomial baseline correction) and results of sensory analysis on bull beef samples (n = 72). Models developed using selected Raman shift ranges (i.e. 250–3380 cm(−1), 900–1800 cm(−1) and 1300–2800 cm(−1)) were explored. The best model performance for each sensory attributes prediction was obtained using models developed on Raman spectral data of 1300–2800 cm(−1).
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spelling pubmed-61416022018-09-21 Performances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributes Zhao, Ming Nian, Yingqun Allen, Paul Downey, Gerard Kerry, Joseph P. O’Donnell, Colm P. Data Brief Chemistry The data presented in this article are related to the research article entitled “Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef” [1]. Partial least squares regression (PLSR) models were developed on Raman spectral data pre-treated using Savitzky Golay (S.G.) derivation (with 2nd or 5th order polynomial baseline correction) and results of sensory analysis on bull beef samples (n = 72). Models developed using selected Raman shift ranges (i.e. 250–3380 cm(−1), 900–1800 cm(−1) and 1300–2800 cm(−1)) were explored. The best model performance for each sensory attributes prediction was obtained using models developed on Raman spectral data of 1300–2800 cm(−1). Elsevier 2018-04-23 /pmc/articles/PMC6141602/ /pubmed/30246069 http://dx.doi.org/10.1016/j.dib.2018.04.056 Text en © 2018 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 Chemistry
Zhao, Ming
Nian, Yingqun
Allen, Paul
Downey, Gerard
Kerry, Joseph P.
O’Donnell, Colm P.
Performances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributes
title Performances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributes
title_full Performances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributes
title_fullStr Performances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributes
title_full_unstemmed Performances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributes
title_short Performances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributes
title_sort performances of full cross-validation partial least squares regression models developed using raman spectral data for the prediction of bull beef sensory attributes
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6141602/
https://www.ncbi.nlm.nih.gov/pubmed/30246069
http://dx.doi.org/10.1016/j.dib.2018.04.056
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