title: Correlation of consumer assessment of longissimus dorsi beef palatability with image colour, marbling and surface texture features.
abstract: A new study was conducted to apply computer vision methods successfully developed using trained sensory panel palatability data to new samples with consumer panel palatability data. The computer vision methodology utilized the traditional approach of using beef muscle colour, marbling and surface texture as palatability indicators. These features were linked to corresponding consumer panel palatability data with the traditional approach of partial least squares regression (PLSR). Best subsets were selected by genetic algorithms. Results indicate that accurate modelling of likeability with regression models was possible (r(2)=0.86). Modelling of other important palatability attributes proved encouraging (tenderness r(2)=0.76, juiciness r(2)=0.69, flavour r(2)=0.78). Therefore, the current study provides a basis for further expanding computer vision methodology to correlate with consumer panel palatability data.
authors.affiliation: FRCFT, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, 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
authors.forename: Karen
authors.initials: K
authors.lastname: Brandon
authors.forename: Anna-Marie
authors.initials: AM
authors.lastname: White
mesh_terms: D000465:Algorithms; D000818:Animals; D002417:Cattle; D003116:Color; D003258:Consumer Behavior; D004041:Dietary Fats; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016018:Least-Squares Analysis; D008460:Meat / Q000032:analysis* / Q000592:standards; D015233:Models, Statistical; D012677:Sensation; D013649:Taste
chemical_list: D004041:Dietary Fats