Grading is an important step for processors, and the industry has traditionally used human graders to measure qualities such as marbling, as well as fat colour and texture. But processors are now moving towards electronic grading tools and technology.
Technologies such as the Computer Vision System’s BeefCam module have achieved up to 64 per cent accuracy in grading beef. However, processors are always looking for more convenient and cost-effective tools that match or improve upon that percentage. A University of Alberta research team led by Heather Bruce has prototypes of two devices that combine the reading of near infrared (NIR) and visible light properties.
The first is a digital scanner for detecting the amount of crude fat and meat colour. The team also created a light meter that detects intramuscular fat in raw beef steaks using visible or NIR light-emitting diodes (LEDs). Both prototypes are hand-held and wireless, and much smaller and manoeuvrable than current tools used in processing plants. Although most current technologies rely on visible light, the team found NIR was better at detecting fat on steaks and could predict intramuscular fat with 90 per cent accuracy. Another promising outcome was that NIR and visible light properties have accuracy similar to human graders.
“We started testing these tools on beef, but we believe that they could be used to grade pork, lamb, and other meats,” said Bruce. “We anticipate that, with the help of industry partners, our light meter and scanner can be refined and perfected for widespread commercial use.”
Their multi-year research was supported by the Alberta Livestock and Meat Agency and Alberta Innovates Bio Solutions.