title: Comparison of the predictive power of beef surface wavelet texture features at high and low magnification.
abstract: Beef longissimus dorsi surface texture is an indicator used in predicting beef palatability by expert graders. Computer vision systems have previously used imaging at normal view to develop surface texture features with some success. Good models of beef overall acceptability using imaging at high magnification have been recently developed. As a comparison the same surface texture features were computed from the corresponding images at normal view and used to model overall acceptability. Both sets of texture features were also combined with muscle colour and marbling features and used to model overall acceptability. Models using texture features alone were more successful at normal modality. However colour and marbling features combined much better with texture features at high modality to yield the most accurate model of overall acceptability (r(2)=0.93). Accurate Partial Least Squares Regression (PLSR) models were computed at both modalities with and without inclusion of colour and marbling features. Addition of squared terms to the models failed to improve accuracy.
authors.affiliation: FRCFT Research Group, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland; Ashtown Food Research Centre, Teagasc, Ashtown, Dublin 15, Ireland.
authors.forename: Patrick
authors.initials: P
authors.lastname: Jackman
authors.forename: Da-Wen
authors.initials: DW
authors.lastname: Sun
authors.forename: Paul
authors.initials: P
authors.lastname: Allen