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South African Journal of Animal Science

On-line version ISSN 2221-4062
Print version ISSN 0375-1589

Abstract

MODIKA, K.Y. et al. Visual evaluation of beef tenderness by using surface structural observations and its relationship to meat colour. S. Afr. j. anim. sci. [online]. 2015, vol.45, n.3, pp.255-262. ISSN 2221-4062.  http://dx.doi.org/10.4314/SAJAS.V45I3.4.

The study describes the relationship between visual and instrumental measurements for colour and tenderness between five South African beef breeds: Bos indicus (Brahman), Sanga type (Nguni), British Bos taurus (Angus), European Bos taurus (Charolais) and the composite (Bonsmara). Ten animals per genotype were used (total = 50). The carcasses were split and the right sides were electrically stimulated, while the left sides were not stimulated. Steaks were aged until three days post mortem on polystyrene plates and until 9, 14 and 20 days post mortem in vacuum bags. The steaks were evaluated by visual analysis for colour, marbling, fibre separation, surface texture and structure integrity by a 10-member trained panel. Colour was also measured by the CIE L*, a*, b* system using a Minolta meter, and tenderness was measured by means of Warner-Bratzler shear force. High negative correlations were observed between the visual colour and L* (r = -0.809), b* (r = -0.698) and high positive correlations were observed between the visual colour and hue (r = 0.797). There were also negative correlations between shear force and structure integrity (r = -0.410) and fibre separation (r = -0.401). Very low negative correlations were observed between colour and shear force (r = -0.242). Therefore, although it may be possible to judge meat colour by visual analysis, it does not appear possible to predict tenderness by colour judgment. There is potential for an experienced eye to predict tenderness by observing visual structural properties such as fibre separation and structural integrity.

Keywords : Meat colour and tenderness; tenderness prediction; trained visual panel; visual analysis.

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