Electromagnetic scanning of pork carcasses in an on-line industrial configuration

Review: Pork belly quality, bacon properties and recent consumer trends

Soladoye, P. O., Shand, P. J., Aalhus, J. L., Gariépy, C. and Juárez, M. 2015. Review: Pork belly quality, bacon properties and recent consumer trends. Can. J. Anim. Sci. 95: 325–340. Several factors can affect pork belly quality and, subsequently, bacon quality. Going by the recent trends in the bacon market and bearing in mind the more choosy nature of the consuming populace, it is imperative to consider the factors that can affect or improve bacon quality, thereby sustaining the current market surge. In as much as both genetic and environmental factors have been identified as largely affecting muscle food quality, nutritional interventions also seem to be a very viable tool to improve the quality of meat and its products. Processing and storage methods can also affect bacon quality, including microbial quality, physicochemical attributes and palatability. Both objective and subjective measures have been explored in assessing belly quality, most of which use belly softness and fatty acid profile as yardsticks, whereas bacon quality has been widely assessed only subjectively in terms of fat quality and slice integrity. Although consumers’ and producers’ quality perceptions seem to be in conflict, it is the responsibility of all stakeholders in the bacon industries to come together in ensuring a balanced approach to satisfy both parties along the production chain.

Electromagnetic scanning to estimate carcass lean content of Taiwan native broilers

To estimate lean content of Taiwan native broiler carcasses accurately, objectively and rapidly, electrical conductivity measurements of broiler carcasses and other relative factors (carcass weight, length and temperature) were used in multiple linear regression analysis for lean prediction. Forty native broiler carcasses, with average market weight 2477.5±465.5 g, were scanned through a 10 MHz electromagnetic field created by an electromagnetic scanner (SA-3203) to measure the total body electrical conductivity (TOBEC) index. After scanning, each broiler carcass was separated into wing, breast, leg and back. Each primal cut was dissected into lean, fat and bone. The weight, length, temperature and TOBEC index of broiler carcass were significantly correlated with lean weight of broiler carcass (P<0.001). Regression analysis for lean estimation with carcass weight, length, temperature and TOBEC index showed higher coefficient of determination (R(2)=0.968) and lower coefficient of variation (CV=4.178) with an equation using beheaded carcass weight, temperature and TOBEC index as variables.

Evaluation of the prediction of alternative measures of pork carcass composition by three optical probes

The accuracy of 3 optical probes (HGP4 Hennessey Grading Probe, Destron-Feering PG-100 probe, and Giraldo OPTO-Electronic PG-200 probe) to predict the carcass percentage of 5 alternative measures of carcass composition (fat-tissue-free lean, lipid-free soft tissue, lipid-free lean, total fat tissue, and soft tissue lipid) was evaluated on 203 barrows and gilts of 7 genetic populations. The optical probe backfat depths were more closely correlated (P < 0.001, 0.963 to 0.983) than the LM depths (r = 0.695 to 0.734). The optical probe backfat depths were related to lean percentage (r = -0.82 to -0.88), total fat tissue percentage (r = 0.84 to 0.88), and soft tissue lipid percentage (r = 0.86 to 0.87). Optical probe LM depths were weakly related (P < 0.05; r = 0.23 to 0.34) to measures of carcass lean percentage and total fat tissue percentage (r = -0.16 to -0.26). Fat-free lean percentage was predicted with residual SD (RSD) of 3.7% for equations including last-rib midline backfat thickness, 2.4 to 2.7% for equations including optical probe backfat and LM depth, and 2.3% for ribbed carcass measurements. The RSD for the optical probe equations ranged from 2.1 to 2.4% for lipid-free soft tissue percentage and from 2.0 to 2.3% for lipid-free lean percentage. The RSD for the optical probe equations ranged from 2.9 to 3.3% for total fat tissue percentage and 2.5 to 2.8% for soft tissue lipid percentage. Quadratic and cross-product variables of optical probe fat depth, LM depth, and carcass weight were significant (P < 0.05) and reduced the RSD of the equations. Optical probe backfat and LM measurements can be used to predict alternative measures of carcass composition. The predicted relationships in fat-free lean percentage to backfat depth were nearly identical for each optical probe.

Evaluation of the impact of errors in the measurement of backfat depth on the prediction of fat-free lean mass

The development of regression equations to predict carcass composition typically assumes that the independent variables, such as backfat depth, are measured without error. However, technological and operator-specific types of measurement errors do exist. To evaluate the impact of measurement error for backfat depth, Monte Carlo simulation was used to model carcass fat-free lean mass (FFLM) in pigs. In the simulation, FFLM was a linear function of carcass weight and actual backfat depth (ABFD). Carcass weight was assumed to be measured without error, but measurement errors were generated such that the correlation (r(BF)) of the measured backfat depth (BFD) and ABFD ranged from 0.70 to 0.95. Two types of measurement errors were simulated: 1) constant variation that was additive to the variance of ABFD, and 2) variation proportional to the ABFD that was additive to the variance in ABFD. A total of 1,000 replications of 1,000 pigs were simulated. Within each type of measurement error, the absolute values of the regression coefficients and R2 values of the equations decreased as r(BF) decreased. The probability of the backfat depth squared (BFD2) being significant (P < 0.05) in the regression equation was increased when the measurement errors were proportional to ABFD. The occurrence of a significant BFD2 variable was 792 times out of 1,000 replications when r(BF) = 0.95 and increased to 996 times out of 1,000 when r(BF) = 0.85 for BFD with type 2 measurement errors. The inclusion of a CW x BFD variable in the regression equations (P < 0.05) increased (270 to 423 times out of 1,000) as r(BF) decreased from 0.85 to 0.70 for BFD with type 2 errors. Equations developed from BFD with measurement errors resulted in biased predictions of FFLM and changes in FFLM per unit change in BFD. The level and type of measurement errors that exist in the independent variables should be evaluated.

Ractopamine treatment biases in the prediction of pork carcass composition

Carcass and live measurements of 45 barrows were used to evaluate the magnitude of ractopamine (RAC) treatment prediction biases for measures of carcass composition. Barrows (body weight = 69.6 kg) were allotted by weight to three dietary treatments and fed to an average body weight of 114 kg. Treatments were: 1) 16% crude protein, 0.82% lysine control diet (CON); 2) control diet + 20 ppm RAC (RAC16); 3) a phase feeding sequence with 20 ppm RAC (RAC-P) consisting of 18% crude protein (1.08% lysine) during wk 1 and 4, 20% crude protein (1.22% lysine) during wk 2 and 3, 16% crude protein (0.94% lysine) during wk 6, and 16% crude protein (0.82% lysine) during wk 6. The four lean cuts from the right side of the carcasses (n = 15/treatment) were dissected into lean and fat tissue. The other cut soft tissue was collected from the jowl, ribs, and belly. Proximate analyses were completed on these three tissue pools and a sample of fat tissue from the other cut soft tissue. Prediction equations were developed for each of five measures of carcass composition: fat-free lean, lipid-free soft tissue, dissected lean in the four lean cuts, total carcass fat tissue, and soft-tissue lipid mass. Ractopamine treatment biases were found for equations in which midline backfat, ribbed carcass, and live ultrasonic measures were used as single technology sets of measurements. Prediction equations from live or carcass measurements underpredicted the lean mass of the RAC-P pigs and underpredicted the lean mass of the CON pigs. Only 20 to 50% of the true difference in fat-free lean mass or lipid-free soft-tissue mass between the control pigs and pigs fed RAC was predicted from equations including standard carcass measurements. The soft-tissue lipid and total carcass fat mass of RAC-P pigs was overpredicted from the carcass and live ultrasound measurements. Prediction equations including standard carcass measurements with dissected ham lean alone or with dissected loin lean reduced the residual standard deviation and magnitude of biases for the three measures of carcass leanmass. Prediction equations including the percentage of lipid of the other cut soft tissue improved residual standard deviation and reduced the magnitude of biases for total carcass fat mass and soft-tissue lipid. Prediction equations for easily obtained carcass or live ultrasound measures will only partially predict the true effect of RAC to increase carcass leanness. Accurate prediction of the carcass composition of RAC-fed pigs requires some partial dissection, chemical analysis, or alternative technologies.