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

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.

SUBMITTER: Zhao M 

PROVIDER: S-EPMC6141602 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature