SUPPLY CHAIN Sarah Stewart Sarita Guy Research Fellow, Murdoch University, WA Research Fellow, Animal Genetics and ALMTech post-doctoral research fellow and livestock Breeding Unit, NSW Department of Primary veterinarian Sarah Stewart is working on program 2 of Industries/University of New England the ALMTech project: ‘Development of eating quality ALMTech post-doctoral research fellow Sarita Guy is working measurement technology’. on program 4 of the ALMTech project: ‘Industry databases’. FB: What is the aim of program 2? FB: What’s the aim of program 4: Industry databases? Sarah: With the advent of automation and new technologies, Sarita: ALMTech is looking to deliver objective carcase we’re looking for devices that will objectively measure traits measurement data on lean meat yield, eating quality and in carcases that can predict eating quality. compliance to market specification. For beef, this will reduce the subjectivity of the human Program 4 aims to facilitate sharing of this data up and grading component of the MSA carcase measurement system down the value chain, to enhance producer feedback, and improve the prediction of consumer eating quality. and to increase genetic gains in lean meat yield and In lamb, there’s no current system for prediction of eating eating quality. quality, so devices that predict key traits like intramuscular fat (IMF) will underpin the development of a cuts-based MSA We’re working with processors to streamline their producer model for lamb. feedback systems, while also helping them analyse and use FB: Which devices are showing the most potential? the data themselves. Sarah: Several devices are showing great promise: FB:How might producers and processors use this data? • Near-infrared (NIR) technology – This technology applies Sarita: Take the use of disease and defect data as an a lens to the cut meat surface and measures the reflected example. A producer might not know they have a problem NIR wavelengths, which vary depending on the chemical because some health conditions are not observable on the composition of the meat. It shows promise in cold carcases live animal, but their animals’ carcases are ending up on the and may enable early carcase segregation before entering processor’s retain rail for trimming. This reduces hot carcase the boning room. weight and therefore payment. By providing understandable and usable feedback, the producer can make more informed • Frontmatec hyperspectral camera – This camera captures management decisions to reduce the risk of these issues, multiple images (at different wave lengths) of the loin eye increase animal welfare and increase profitability for muscle and involves segregating fat from muscle and themselves and processors. bone, allowing for the prediction of multiple traits including IMF, meat and fat colour, eye muscle area and fat depth of For the processor, poor health is costly due to extra the carcase at the 12th rib site on the ribbed cold carcase. labour, slower chain speed and loss of product. Analysing About 1,000 lamb and beef images have been taken in the the data they collect could help them identify seasonal past year, using MLA Resource Flock and Beef Information trends and high-risk periods for particular health Nucleus herds. Image analysis for lamb clearly shows the conditions, and allocate staff accordingly to keep the chain potential of this technology to measure IMF. operating efficiently. ■ Sarah Stewart Sarita Guy E: s.stewart@murdoch.edu.au E: sarita.guy@une.edu.au 39