Green, Melissa Dianne, authorBelk, Keith E., advisorTatum, Joseph D., committee memberChapman, Phillip L., committee member2022-04-132022-04-132010https://hdl.handle.net/10217/234678Covers not scanned.Print version deaccessioned 2022.The research presented herein was conducted to update BeefCam™ tenderness predictive abilities of 14 day aged longissimus muscle samples by creating new regression equations. In this investigation, image data were collected from 670 carcasses at four beef packing plants using a video image analysis system, BeefCam™, and those data were used to predict the tenderness of aged (14 d), fresh beef Longissimus muscle (EM). Portions of the EM were removed from the striploin subprimal (NAMP #180) on both sides of each carcass. All EM samples remained fresh, were aged at 2°C for 14 d, and were cooked to a target internal temperature of 71°C. The EM samples collected from the right side of each carcass were assessed for tenderness by means of Warner-Bratzler shear force (WBSF) analysis, whereas samples collected from the left side of each carcass were evaluated by means of slice shear force (SSF) analysis. Data were sorted by SSF values and half the carcasses from each day of collection were utilized as a sequestered validation dataset (N = 334), while the remaining 336 carcasses constituted an instrument calibration dataset. BeefCam™ output measures were used in regression analyses to predict beef EM tenderness following aging. A regression equation was developed using the calibration dataset that correctly classified 280 carcasses out of 336 (83.3%) as tough or tender based on EM tenderness. When the same equation was applied to the sequestered validation dataset, it correctly classified 266 out of 334 (79.6%) carcasses as tough or tender. The developed regression equation was very successful in classifying tender carcasses, although BeefCam™ had difficulty properly identifying the tough carcasses. The root mean square error (RMSE), predicted residual sum of squares (PRESS) and statistics for the regression model were 0.1239, 2.418 and 0.3300, respectively. BeefCam™ repeatability has previously been verified and approved by USDA-AMS, but in this study, repeatability was determined to be 92.6% for the calibration dataset (N = 314) when a novice operated the instrument.masters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.Beef -- QualityBeef cattle -- Carcasses -- CompositionText