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United States Department of Agriculture

Agricultural Research Service

Research Project: STRATEGIES TO OPTIMIZE CARCASS YIELD AND MEAT QUALITY OF RED MEAT ANIMALS Title: Non-invasive Prediction of Pork Loin Tenderness

Authors
item Shackelford, Steven
item King, David
item Wheeler, Tommy

Submitted to: Journal of Animal Science Supplement
Publication Type: Abstract Only
Publication Acceptance Date: February 11, 2010
Publication Date: March 15, 2010
Citation: Shackelford, S.D., King, D.A., Wheeler, T.L. 2010. Non-invasive Prediction of Pork Loin Tenderness [abstract]. Journal of Animal Science Supplement. 88(E-Suppl. 3). Abstract #122.

Interpretive Summary: Not required.

Technical Abstract: The present experiment was conducted to develop a non-invasive method to predict tenderness of pork loins. Boneless pork loins (n = 901) were evaluated either on line on the loin boning and trimming line of large-scale commercial plants (n = 465) or at the U.S. Meat Animal Research Center abattoir (n = 436). Exposed longissimus on the ventral side of boneless loins was evaluated with visible and near-infrared spectroscopy (VISNIR; 450 to 1000 nm) using a commercial system that was developed for on-line evaluation of beef tenderness. Boneless loin sections were aged (2°C) until 14 days postmortem and two 2.54-cm thick chops were obtained from the 11th rib region. Fresh (never frozen) chops were cooked (71°C) and longissimus slice shear force (SSF) was measured on each of the two chops. Those two values were averaged and that value was used for all analyses. Carcasses were blocked by plant (n = 3), production day (n = 24), and observed SSF (Mean = 13.9 kg; SD = 3.7; CV = 26.8%; Range 6.4 to 32.4 kg) and one-half of the carcasses were assigned to a calibration data set (CDS), which was used to develop regression equations, and one-half of the carcasses were assigned to a prediction data set (PDS), which was used to validate the regression equations. A partial least-squares regression model was developed and loins were classified as "Predicted Tender" (PT) or "Not Predicted Tender" (NPT) if their VISNIR-predicted SSF was < 14.0 kg or > 14.0 kg, respectively. ANOVA was used to determine the effect of VISNIR classification on SSF. The CDS and PDS had 61.9% and 60.9% of the loins classified as PT, respectively. For both the CDS and PDS, mean SSF was lower for PT than NPT (P < 0.001). Likewise, the percentage of loins with SSF > 20 kg was lower for PT than NPT in the CDS (3.6 vs 8.1%) and PDS (1.8 vs 13.6%). These results clearly indicate that the VISNIR technology could be used to non-invasively classify pork loins on-line for tenderness.

Last Modified: 12/21/2014
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