Do carcass traits influence consumer perception of pork eating quality?

The objective of this study was to assess carcass traits' influence on pork eating quality as evaluated by consumers. A total of 1360 pork chops were used, with 824 from the sirloin end and 536 from the butt end of the loin (Longissimuss thoracis et lumborum), to produce 340 packages, each containing four pork chops. Untrained participants received one package of either sirloin or butt chops, being two pork chops from barrows and two from gilts. Participants answered a survey rating the tenderness, juiciness, flavour, and overall acceptability of each chop on an 8-point scale. Correlation analysis was conducted between carcass traits and pork eating quality attributes. For the descriptive analysis, classes (low, medium, and high) for carcass traits, Warner-Bratzler shear force (WBSF) and cooking loss were created based on our consumer responses dataset for palatability attributes. No significant correlations (P > 0.05) were observed between carcass traits and pork eating quality traits. Tenderness and overall acceptability were negatively correlated (P < 0.05) with cooking loss and WBSF. Loin intramuscular fat (IMF) content showed a weak negative correlation (P < 0.05) with WBSF and cooking loss. Consumers rated chops from the high and medium/high backfat thickness and loin IMF classes slightly higher for tenderness and juiciness, respectively. Additionally, chops from the low and/or medium WBSF and cooking loss classes received slightly higher scores for tenderness and juiciness than pork chops in the high classes. In conclusion, the study indicated that carcass traits had minimal impact on overall acceptability of pork by consumers.

A comparison of carcass characteristics, carcass cutting yields, and meat quality of barrows and gilts.

Objectives of this research were to compare carcass characteristics, carcass cutting yields, and meat quality for market barrows and market gilts. Commercially-sourced carcasses from 168 market barrows and 175 market gilts weighing an average of 107.44 ± 7.37 kg were selected from 17 different slaughter groups representing approximately 3,950 carcasses. Each group was sorted into percentiles based on hot carcass weight with an equal number of barrows and gilts selected from each quartile so that weight minimally confounded parameters of interest. Carcass lean yield was determined for carcasses following fabrication (i.e. dissection of lean, fat, and bone tissue components) and meat quality measurements were evaluated at the time of fabrication (24 to 72 h postmortem) and following 14-d of postmortem storage. Data were analyzed as a randomized complete block design with carcass serving as the experimental unit, sex (barrow or gilt), the three hot carcass weight quantiles (light [<104 kg]; average [104 to 110 kg]; heavy [>110 kg]), and the interaction between sex and hot carcass weight quantile serving as fixed effects, and producer nested within slaughter event serving as a random effect. Results from the study demonstrated that gilt carcasses were leaner (3 mm less backfat thickness; 3.5 cm2 greater loin muscle area, 1.52% greater merchandized-cut yield, and 2.92% greater dissected carcass lean yield; P < 0.01) than barrow carcasses, while loins from barrows were higher quality (0.43% more intramuscular fat and slightly less shear force; P < 0.01) than loins from gilts. While this study confirms the well-known biological principle that barrow carcasses have greater levels of fat deposition and lower levels of carcass leanness when compared with gilt carcasses, this study provides a much-needed quantification of these differences for the commercial industry that will undoubtedly be useful as new technologies emerge in upcoming years.

An update of the predicted lean yield equation for the Destron PG-100 optical grading probe.

The objective was to update the equation used for prediction of pork carcass leanness with the Destron PG-100 optical grading probe. A recent cutout study (completed in 2020-2021) consisting of 337 pork carcasses was used for this research. An updated equation was generated using a calibration dataset (N = 188 carcasses) and prediction precision and prediction accuracy of the new equation was evaluated using a validation dataset (N = 149 carcasses). The updated equation was generated using forward stepwise multiple regression selection techniques in PROC REG of SAS, and the same parameters as the existing equation were used to fit the model. The updated Destron equation [89.16298 - (1.63023 × backfat thickness) - (0.42126 × muscle depth) + (0.01930 × backfat thickness2) + (0.00308 × muscle depth2) + (0.00369 × backfat thickness × muscle depth)] and the existing Destron equation [68.1863 - (0.7833 × backfat thickness) + (0.0689 × muscle depth) + (0.0080 × backfat thickness2) - (0.0002 × muscle depth2) + (0.0006 × backfat thickness × muscle depth)] were similar in their prediction precision for determination of carcass lean yield (LY), with the updated equation R2 = 0.75 and root mean square error (RMSE) = 1.97 and the existing equation R2 = 0.75 and RMSE = 1.94. However, when prediction accuracy was evaluated using the variance explained by predictive models based on cross-validation (VEcv) and Legates and McCabe's efficiency coefficient (E1), the updated equation (VEcv = 67.97%; E1 = 42.41%) was much more accurate compared with the existing equation (VEcv = -117.53%; E1 = -69.24%). Furthermore, when accuracy was evaluated by separating carcasses into 3% carcass LY groupings ranging from less than 50% LY to greater than 62% LY, the existing equation correctly estimated carcass LY 8.1% of the time, while the updated equation correctly estimated carcass LY 47.7% of the time. In an effort to further compare the abilities of the updated equation, comparisons were made with an advanced automated ultrasonic scanner (AutoFom III), which scans the entire carcass. The prediction precision of the AutoFom III was R2 = 0.83 and RMSE = 1.61, while the AutoFom III correctly estimated carcass LY 38.2% of the time and prediction accuracy calculations for the AutoFom III were VEcv = 44.37% and E1 = 21.34%). Overall, refinement of the Destron PG-100 predicted LY equation did not change prediction precision, but substantially improved prediction accuracy.

In the swine industry, optical grading probes are used to collect measurements at one location on the carcass (i.e., the grading site); this information is used to predict carcass leanness with an established multiple regression equation. For the Destron PG-100 optical probe, the equation currently used by the industry was established using the 1992 Canadian National Cutout Study. The objective of the current study was to update the equation by utilizing a recent cutout study consisting of 337 pork carcasses (N = 188 for the calibration dataset; N = 149 for the validation dataset). A multiple regression equation was generated with the calibration dataset using stepwise techniques and the same parameters as the existing equation were used to fit the model. When the validation dataset was tested for prediction precision, the existing Destron predicted lean yield (LY) equation (R2 = 0.75) and the updated Destron predicted LY equation (R2 = 0.75) were strikingly similar in their abilities to predict carcass LY. However, when prediction accuracy was evaluated by separating carcasses into six 3% LY groupings, the existing Destron predicted LY equation correctly estimated LY 8.1% of the time, while the updated Destron predicted LY equation correctly estimated LY 47.7% of the time.

Comparison of an advanced automated ultrasonic scanner (AutoFom III) and a handheld optical probe (Destron PG-100) to determine lean yield in pork carcasses.

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.

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.