Sensory characteristics of tenderness, juiciness and flavour of beef are important criteria that affect consumer’s evaluation of beef quality. Near infrared reflectance spectroscopy (NIR) may be an effective tool to predict sensory characteristics of beef meat. This method is non-destructive, fast and potentially suitable for on-line measurements. The association between NIR measurements and sensory characteristics is expected to be based on chemical (e.g. water, lipid and protein content) and physical or structural (e.g. muscle fibre characteristics) properties of the meat. Objective measurements of physical or structural characteristics can be obtained using Volodkevitch shear force or slice shear force techniques (Shackelford et al., 1999). The objective of this study was to investigate the associations between eating quality traits of beef and NIR measurements.

Predictors of carcass and meat quality are sought that can be measured in the live animal, preferably at a young age, on-farm, and with minimum stress to the growing animal. Several in vivo methods of assessing these traits indirectly in lambs have been developed, most of which are expensive and require restraint/handling of animals, which can cause stress and affect growth rate. This study investigates the use of linear dimensions obtained from video image analysis (VIA) to assess live weight, carcass composition and meat quality (MQ) in lambs of two divergent breeds, in a non-disruptive way.