Performances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributes
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Authors
Zhao, Ming
Nian, Yingqun
Allen, Paul
Downey, Gerard
Kerry, Joseph P.
O'Donnell, Colm P.
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Elsevier Ltd
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Abstract
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” (Zhao et al., 2018) [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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Keywords
Selected raman shift ranges , Sensory attributes , Bull beef , Partial least squares regression models
Citation
Zhao, M., Nian, Y., Allen, P., Downey, G., Kerry, J. P. and O’Donnell, C. P. (2018) 'Performances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributes', Data in Brief. doi: 10.1016/j.dib.2018.04.056