This study compared the accuracy of two methods for predicting carcass leanness (i.e., predicted lean yield) with fat-free lean yields obtained by manual carcass side cut-out and dissection of lean, fat, and bone components. The two prediction methods evaluated in this study estimated lean yield by measuring fat thickness and muscle depth at one location with an optical grading probe (Destron PG-100) or by scanning the entire carcass with advanced ultrasound technology (AutoFom III). Pork carcasses (166 barrows and 171 gilts; head-on hot carcass weights (HCWs) ranging from 89.4 to 138.0 kg) were selected based on their fit within desired HCW ranges, their fit within specific backfat thickness ranges, and sex (barrow or gilt). Data (n = 337 carcasses) were analyzed using a 3 × 2 factorial arrangement in a randomized complete block design including the fixed effects of the method for predicting lean yield, sex, and their interaction, and random effects of producer (i.e., farm) and slaughter date. Linear regression analysis was then used to examine the accuracy of the Destron PG-100 and AutoFom III data for measuring backfat thickness, muscle depth, and predicted lean yield when compared with fat-free lean yields obtained with manual carcass side cut-outs and dissections. Partial least squares regression analysis was used to predict the measured traits from image parameters generated by the AutoFom III software. There were method differences (P < 0.01) for determining muscle depth and lean yield with no method differences (P = 0.27) for measuring backfat thickness. Both optical probe and ultrasound technologies strongly predicted backfat thickness (R2 ≥ 0.81) and lean yield (R2 ≥ 0.66), but poorly predicted muscle depth (R2 ≤ 0.33). The AutoFom III improved accuracy [R2 = 0.77, root mean square error (RMSE) = 1.82] for the determination of predicted lean yield vs. the Destron PG-100 (R2 = 0.66, RMSE = 2.22). The AutoFom III was also used to predict bone-in/boneless primal weights, which is not possible with the Destron PG-100. The cross-validated prediction accuracy for the prediction of primal weights ranged from 0.71 to 0.84 for bone-in cuts and 0.59 to 0.82 for boneless cut lean yield. The AutoFom III was moderately (r ≤ 0.67) accurate for the determination of predicted lean yield in the picnic, belly, and ham primal cuts and highly (r ≥ 0.68) accurate for the determination of predicted lean yield in the whole shoulder, butt, and loin primal cuts.
Keywords: AutoFom; Destron; carcass prediction; lean yield; pork grading; ultrasound.
Pork grading is a producer-feedback system that provides carcass trait information (i.e., carcass weight, fat/lean deposition) to determine the economic value of carcasses. Packing plants generally emphasize the optimization of carcass weight and leanness by providing premium or discounted prices using a grid system. Packing plants routinely collect carcass weights while carcass leanness can be more challenging to capture. Since the packing industry does not measure fat/lean deposition for each carcass or each meat cut within the carcass, various technologies are used to predict carcass leanness. These include optical probes, spectral imaging, artificial vision, and others that have been around for decades. A challenge with these technologies is that they often collect measurements at only one location on the carcass, providing information that is not necessarily representative of the entire carcass. The purpose of this study was to compare the accuracy of an advanced automated ultrasonic scanner (AutoFom III) that scans the entire carcass with that of a handheld optical probe (Destron PG-100) that collects measurements from one location on the carcass. In summary, the AutoFom III improved accuracy for determining lean yield with the additional advantage of predicting primal weights when compared with the Destron PG-100.
© The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.