Changing Australia's trading language has enhanced the implementation of objective carcase measurement technologies

In 2016 an Australian project, the Advanced Livestock Measurement Technologies project (ALMTech), was initiated to accelerate the development and implementation of technologies that measure lean meat yield and eating quality. This led to the commercial testing, and implementation of a range of new technologies in the lamb, beef, and pork industries. For measuring lean meat yield %, these technologies included dual energy X-ray absorptiometry, hand-held microwave systems, and 3-D imaging systems. For measuring beef rib-eye traits and intramuscular fat %, both pre- and post-chilling technologies were developed. Post-chilling, a range of camera systems and near infrared spectrophotometers were developed. While pre-chilling, technologies included insertable needle probes, nuclear magnetic resonance, and X-ray systems. Initially these technologies were trained to predict the pre-existing traits already traded upon within industry. However, this approach was limiting because the technologies could measure attributes that were either non-existent in the trading language, were superior as calibrating standards, or more accurately reflected value than the pre-existing trait. Therefore, we introduced IMF% into the trading language for both beef and sheep meat, and carcase lean%, fat%, and bone% for sheep meat. These new technologies and the traits that they predict have delivered multiple benefits. Technology provider-companies are instilled with the confidence to commercialise due to the provision of achievable accreditation standards. Processors have the confidence to invest in these technologies and establish payment grids based upon their measurements. And lastly, it has enhanced data flow into genetic databases, industry data systems (MSA), and as feedback to producers.

Review: Improving the nutritional, sensory and market value of meat products from sheep and cattle

This paper focuses on improving the sensory, health attributes and meat yield of beef and lamb meats. Value for meat is defined as the weight of meat × price/kg received with price linked to eating quality. To maximise value across the supply chain, accurate carcass grading systems for eating quality and yield are paramount. Grading data can then be used to target consumers' needs at given price points and then to tailor appropriate production and genetic directions. Both the grading methodologies and key phenotypes are complex and still under intensive research with international collaboration to maximise opportunities. In addition, there is value in promoting the health aspects of red meats served as whole trimmed meats. Typically, the total fat content is relatively low (less than 5%) and for forage systems, they deliver a very significant content of long-chain n-3 fatty acids. Further research is needed to clarify the healthiness or otherwise of ground beef served as burgers given the fat content is typically 20% or more. It is important to continue to improve the feedback to producers regarding the quantity and quality of the products they produce to target new value opportunities in a transparent and quantitative manner.

Development of VISNIR predictive regression models for ultimate pH, meat tenderness (shear force) and intramuscular fat content of Australian lamb

This study investigated the effectiveness of visible-near-infrared (VISNIR) spectroscopy at classifying Australian lamb for: a) ultimate pH (pH 24), b) meat tenderness (i.e. shear force at day 5 of ageing, SF5) and c) intramuscular fat (IMF) content at 24 h post-slaughter using a custom-made handheld probe coupled with the ASD Labspec Pro instrument. VISNIR predictive regression models were developed. In the loin muscle (M. longissimus thoracis et lumborum), the models classified the predicted pH 24, SF5 and IMF content at above or below a threshold value with 94%, 98% and 88% accuracy, respectively. The observed difference between the actual and predicted value (i.e. the standard error of cross validation, SECV) for ultimate pH and IMF content are approaching accuracies required to attain highly reliable Meat Standards Australia grading standards. However, further development is required to improve the SECV for SF5.