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

Ming Zhao; Yingqun Nian; Paul Allen; Gerard Downey; Joseph P Kerry; Colm P O'Donnell

doi:10.1016/j.dib.2018.04.056

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

Data Brief. 2018 Apr 23:19:1355-1360. doi: 10.1016/j.dib.2018.04.056. eCollection 2018 Aug.

Authors

Ming Zhao¹, Yingqun Nian^{2

3}, Paul Allen², Gerard Downey¹, Joseph P Kerry³, Colm P O'Donnell¹

Affiliations

¹ School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland.
² Department of Food Quality and Sensory Science, Teagasc Food Research Centre, Ashtown, Dublin 15, Ireland.
³ School of Food and Nutritional Sciences, University College Cork, Cork, Ireland.

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" [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.

Keywords: Bull beef; Partial least squares regression models; Selected Raman shift ranges; Sensory attributes.