This Small Business Innovation Research Phase I project seeks to determine the feasibility of a multispectral imaging technology, to provide real time beef tenderness prediction. The hypothesis is that it is possible to predict the tenderness of beef using only a moderate number of bands for the image. This is based on the observation that hyperspectral images contain large amounts of redundant information. The technological challenge is to verify the hypothesis by identifying the key bands that are central to tenderness prediction and to develop a model to accurately predict tenderness. Identification of key bands will be performed using data mining and image analysis algorithms. Successful verification of the hypothesis will directly lead to the development of a multispectral system for tenderness prediction at commercial speeds.
Currently there are limited methods for real time classification of beef by tenderness, the most important trait influencing consumer satisfaction. Meat packers have expressed a desire to sort by tenderness, because consumers are willing to pay a premium for steaks that are guaranteed tender (an added value of $170 to $421 per carcass certified as tender). Some of the major packers have expressed interest in a service to classify beef for tenderness. At 2.5% of value added per head for 20% of the cattle, annual revenue of $23.7 million to $58.7 million could be generated from service sales to the top four U.S. meat packers. International potential would magnify this effect. This project, if successful, is expected to enhance economic opportunities for cattle producers and processors by improving assessment of beef product quality to meet consumer expectations.
This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
