Hyperspectral Imaging: A Non-Invasive Technique to Predict Beef Tenderness

Authors

Lauren Grimes, University of Nebraska-Lincoln
Govindarajan Konda Naganathan, University of Nebraska-LincolnFollow
Jeyamkondan Subbiah, University of Nebraska-LincolnFollow
Chris R. Calkins, University of Nebraska-LincolnFollow

Date of this Version

1-1-2007

Comments

Published in 2007 Nebraska Beef Cattle Report. Copyright © 2006 The Board of Regents of the University of Nebraska.

Abstract

A hyperspectral imaging apparatus was developed and assembled to predict 14 d tenderness of beef steaks. USDA Choice and Select grade longissimus steaks (n=111) from between the 12th and 13th ribs were frozen at 14 days post-mortem, cut to 1-inch thickness, and thawed overnight for scanning, cooking, and obtaining slice shear force data. The model predicted three tenderness categories with 96.4% accuracy, correctly classifying 93 tender, nine intermediate and all five tough samples. One tender sample was misclassified as intermediate, and three intermediate samples were misclassified as tender. This hyperspectral imaging system was effective in predicting beef tenderness.