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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Selected Raman shift rangesSensory attributes
Bull beef
Partial least squares regression models
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2018-04-23
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Zhao M, Nian Y, Allen P, Downey G, Kerry JP, O’Donnell CP. 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 2018;19:1355-1360; doi https://doi.org/10.1016/j.dib.2018.04.056Abstract
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.Funder
Teagasc Walsh Fellowship Programmeae974a485f413a2113503eed53cd6c53
https://doi.org/10.1016/j.dib.2018.04.056
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