SBIR-STTR Award

Beef tenderness is the most important trait influencing consumer satisfaction. Currently there is no method for real-time classification of beef by tenderness. The overall objective of this project is to determine the feasibility of a multispectral image acquisition system for real-time forecasting of beef tenderness (forecast 14-day tenderness from 2-day images). We have demonstrated that hyperspectral imaging (with 91 wavelength bands) can forecast beef tenderness with high accuracy. However, the image capture time and the analysis of the massive amounts of hyperspectral image data make this technology unrealistic for real-time implementation. To make this technology commercially feasible, we have identified and validated eight key wavelength bands that are central to beef tenderness forecasting. A multispectral imaging system capable of rapidly acquiring the key wavelength bands on moving beef carcasses will be tested for forecasting beef tenderness. These efforts will lead to a rapid and accurate multispectral imaging technology for real-time tenderness forecasting in commercial beef packing plants. Meat packers are anxious to sort beef by tenderness because consumers are willing to pay a premium for steaks that are guaranteed tender. All of the major packers have expressed interest in a service to classify beef for tenderness. Economists estimate the domestic economic value of a USDA certified tender beef program to be as much as $760 million per year. Capturing 5% of this value using the proposed technology would return an annual revenue of $38 million. International potential would magnify this effect. This project is expected to enhance economic opportunities for cattle producers in rural America and processors by improving assessment of beef product quality to meet consumer expectations.

Beef tenderness is the most important trait influencing consumer satisfaction. Currently there is no method for real-time classification of beef by tenderness. Current beef marketing is based on USDA quality grades (Prime, Choice, and Select). Therefore, the beef industry needs an integrated system that can simultaneously predict beef tenderness and also USDA grades. In the Phase I study, we successfully demonstrated the feasibility of predicting beef tenderness and USDA grades with a multispectral imaging system. However, in order to commercialize our technology, we need to (1) develop, refine, and integrate functionality for predicting USDA grades concurrent with beef tenderness, (2) evaluate accuracy and repeatability of the system with a large dataset, and (3) increase the prediction speed of the system. The overall goal of this Phase II proposal is to make the multispectral imaging system used in Phase I ready for commercial use. Meat packers are anxious to sort beef by tenderness because consumers are willing to pay a premium for steaks that are guaranteed tender. All of the major packers have expressed interest in a service to classify beef for tenderness. Economists estimate the domestic economic value of a USDA certified tender beef program to be as much as $760 million per year. Capturing a small portion of this value using the proposed technology would return annual revenue of $28 million. International potential would magnify this effect. This project is expected to enhance economic opportunities for cattle producers in rural America and processors by improving assessment of beef product quality to meet consumer expectations.