Correlation of consumer assessment of longissimus dorsi beef palatability with image colour, marbling and surface texture features

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 (r2 = 0.86). Modelling of other important palatability attributes proved encouraging (tenderness r2 = 0.76, juiciness r2 = 0.69, flavour r2 = 0.78). Therefore, the current study provides a basis for further expanding computer vision methodology to correlate with consumer panel palatability data.