Prediction of beef eating qualities from colour, marbling and wavelet surface texture features using homogenous carcass treatment

  • Authors:

  • Patrick Jackman;Da-Wen Sun;Cheng-Jin Du;Paul Allen

  • Affiliations:

  • FRCFT, University College Dublin, National University of Ireland, Agriculture & Food Science Centre, Belfield, Dublin 4, Ireland and Ashtown Food Research Centre, Teagasc, Ashtown, Dublin 15, Irel ...;FRCFT, University College Dublin, National University of Ireland, Agriculture & Food Science Centre, Belfield, Dublin 4, Ireland;FRCFT, University College Dublin, National University of Ireland, Agriculture & Food Science Centre, Belfield, Dublin 4, Ireland;Ashtown Food Research Centre, Teagasc, Ashtown, Dublin 15, Ireland

  • Venue:
  • Pattern Recognition
  • Year:
  • 2009

Quantified Score

Hi-index 0.01

Visualization

Abstract

Colour, marbling and surface texture properties of beef longissimus dorsi muscle are used in some countries to grade carcasses according to their expected eating quality. Handheld VIA systems are being used to augment the grader assessments, however attempts have been made to develop higher resolution image systems to give consistent and objective predictions of quality based on these properties. Previous efforts have been unable to model sufficiently the variation in eating quality. A new approach has been applied whereby beef carcasses were subjected to homogenous post-slaughter treatment to minimize variation in eating quality related to other factors such as chilling temperature and hanging method. Furthermore a wider range of features were used to better characterize colour and marbling and the wavelet transform was used to characterize texture. Objective and sensory panel tests were performed to evaluate the beef eating qualities. Classical statistical methods of multilinear regression (MLR) and partial least squares regression (PLSR) were used to develop predictive models. It was possible to explain a greater portion of variation in eating quality than before (up to r^2=0.83). Carcasses were classified as high or low quality with a high rate of correct classifications (90%). Genetic algorithms were used to select the model subsets.