Computer image analysis for prediction of carcass composition from cross-sections of Japanese Black steers.

Carcasses from Japanese Black steers were used to obtain prediction equations for carcass composition from information derived by computer image analysis of carcass cross-section images. The total weights of lean, fat, and bone were obtained from the left sides of 55 carcasses (Data Set I) and 18 carcasses (Data Set II) by physical dissection. The information such as total lean, fat, and bone areas in the cross-sections; muscle area, muscle circumference, short and long radius axis lengths, and direction of long radius axis; and geometric distance between any two muscle centers of gravity was obtained by scanning and image analysis of pictures of the cross-sections of the beef side at the 6th/7th rib interface. The coefficients of determination of the multiple regression equations estimated from Data Set I for kilograms of lean, fat, and bone were 0.76, 0.82, and 0.69, respectively, whereas for the percentages of lean, fat, and bone they were 0.57, 0.66, and 0.42, respectively. The multiple regression equations from Data Set I was applied to Data Set II in order to test the applicability of the prediction equations obtained. The correlation coefficients between the value predicted by the multiple regression equation and the measurement obtained by physical dissection for kilograms of lean, fat, and bone were 0.71, 0.72, and 0.70, respectively, whereas those for the percentages of lean, fat, and bone were 0.63, 0.44, and 0.29, respectively. The results indicate that the information obtained from the carcass cross-sections by the computer image analysis method can be used to predict carcass composition in Japanese Black steers.     

Studies of the spectrum of nonesterified fatty acids in blood and muscle tissue of swine. 1. Effect of genotype (muscular substance)

The percentual composition of unesterified fatty acids in M. longissimus dorsi was determined by means of gas chromatography, and its correlations with the given genotype and pork condition were studied. Polyene fatty acids recorded from the Piétrain race deviated (C-18:2, C-18:3, C-20:4) from values recorded from large white and improved land race pigs. Animals of PSE nature were like Piétrain probands, in that their monoene acids were higher and diene acids lower by percentage than the comparable values in animals of normal pork condition.     

Carcass traits, cut yields, and compositional end points in high-lean-yielding pork carcasses: effects of 10th rib backfat and loin eye area

Pork carcasses (n = 133) were used to investigate the influence of carcass fatness and muscling on composition and yields of pork primal and subprimal cuts fabricated to varying levels of s.c. fat. Carcasses were selected from commercial packing plants in the southeastern United States, using a 3 x 3 factorial arrangement with three levels of 10th rib backfat depth (< 2.03, 2.03 to 2.54, and > 2.54 cm) and three levels of loin eye area (LEA; < 35.5, 35.5 to 41.9, and > 41.9 cm2). Sides from the selected carcasses were shipped to the University of Georgia for carcass data collection by trained USDA-AMS and University of Georgia personnel and fabrication. Sides were fabricated to four lean cuts (picnic shoulder, Boston butt, loin, and ham) and the skinned belly. The four lean cuts were further fabricated into boneless cuts with s.c. fat trim levels of 0.64, 0.32, and 0 cm. The percentages of four lean cuts, boneless cuts (four lean cuts plus skinned, trimmed belly) at 0.64, 0.32, and 0 cm s.c. fat, fat-free lean, and total fat were calculated. Data were analyzed using a least squares fixed effects model, with the main effects of 10th rib backfat and LEA and their interaction. Fatness and muscling traits increased (P < 0.05) as 10th rib backfat and LEA category increased, respectively. However, fat depth measures were not affected greatly by LEA category, nor were muscling measures greatly affected by backfat category. The percentage yield of cuts decreased (P < 0.05) as backfat category increased. Cut yields from the picnic shoulder, Boston butt, and belly were not affected (P > 0.05) by LEA category, whereas the yield of boneless loin and ham increased (P < 0.05) as LEA category increased. Compositionally, the percentage of four lean cuts, boneless cuts at varying trim levels, and fat-free lean decreased incrementally (P < 0.05) as backfat depth increased, whereas parentage total fat and USDA grade increased (P < 0.05) as backfat depth increased. As LEA increased, percentage boneless cuts trimmed to 0.32 and 0 cm s.c. fat and fat-free lean increased and total fat decreased; however, the difference was only significant in the smallest LEA category. Collectively, these data show that decreased carcass fatness plays a greater role in increasing primal and subprimal cut yields and carcass composition than muscling even in lean, heavily muscled carcasses.     

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.     

Comparison of market hog characteristics of pigs selected by feeder pig frame size or current USDA feeder pig grade standards

Two feeder pig grading systems were tested. Forty-five barrows were selected using current USDA Feeder Pig Grade Standards (U.S. No. 1, No. 2 and No. 3). Additionally, 45 barrows were selected using three frame sizes (large, medium and small). Pigs were slaughtered at 100, 113.5 of 127 kg live weight. Trimmed four lean cuts were separated into soft tissue, skin and bone. The skinless belly and soft tissue from the four lean cuts were ground separately and analyzed chemically. Data from each grading system were analyzed separately in a 3 X 3 factorial plan. Pigs selected using current USDA grade standards differed (P less than .05) for last rib backfat, 10th rib fat depth, longissimus muscle area, percentage of trimmed four lean cuts and USDA carcass grade. In the frame size system, pigs with large frame size had less last rib backfat, less 10th rib fat depth, longer carcasses, higher percentage of four lean cuts and superior USDA carcass grades than pigs with small frame size did (P less than .05). The Bradley and Schumann test of sensitivity showed that selection by frame size was more sensitive than current USDA grade standards for discriminating feeder pig foreleg length, body depth and ham width. In addition, selection by frame size was more sensitive than current USDA grade standards for discriminating carcass length and carcass radius length. No increase in sensitivity (P greater than .10) was noted for carcass composition or growth traits over the current USDA Feeder Pig Grade Standards.     

Intramuscular fat content and genetic variants at fatty acid-binding protein loci in Austrian pigs.

Intramuscular fat is an important meat quality trait in pig production. Previously, genetic variants of the heart fatty acid-binding protein (H-FABP) gene and the adipocyte fatty acid-binding protein (A-FABP) gene were suggested to be associated with intramuscular fat content. The objective of this investigation was to study these associations in the three most important Austrian breeding populations (Piétrain, Large White, and Landrace). Restriction fragment length polymorphism analysis of the H-FABP gene revealed a new MspI polymorphic site and genetic variation in all three breeds. Microsatellite analysis of the A-FABP locus showed up to nine different microsatellite alleles segregating. In Austrian breeds, no significant influence of the A-FABP and H-FABP gene polymorphisms on intramuscular fat could be detected. We also evaluated possible associations between the genetic variations at the H-FABP and A-FABP loci and other growth and carcass traits (average daily gain, feed conversion ratio, lean meat content, pH values, meat color, and drip loss). With regard to the extent of the effects, these genetic markers cannot be recommended for selection on growth and carcass traits in Austrian breeding populations.     

Gluteus medius and rump fat depths as additional live animal ultrasound measurements for predicting retail product and trimmable fat in beef carcasses.

This study was conducted to determine the ability of additional ultrasound measures to enhance the prediction accuracy of retail product and trimmable fat yields based on weight and percentage. Thirty-two Hereford-sired steers were ultrasonically measured for 12th-rib fat thickness, longissimus muscle area, rump fat thickness, and gluteus medius depth immediately before slaughter. Chilled carcasses were evaluated for USDA yield grade factors and then fabricated into closely trimmed, boneless subprimals with 0.32 cm s.c. fat. The kilogram weight of end-point product included the weight of trimmed, boneless subprimals plus lean trim weights, chemically adjusted to 20% fat, whereas the fat included the weight of trimmed fat plus the weight of fat in the lean trim. Prediction equations for carcass yield end points were developed using live animal or carcass measurements, and live animal equations were developed including ultrasound ribeye area or using only linear measurements. Multiple regression equations, with and without ultrasound rump fat thickness and gluteus medius depth, had similar R2 values when predicting kilograms of product and percentages of product, suggesting that these alternative variables explained little additional variation. Final unshrunk weight and ultrasound 12th-rib fat thickness explained most of the variation when predicting kilograms of fat. Rump fat and gluteus medius depth accounted for an additional 10% of the variation in kilograms of fat, compared with the equation containing final weight, ultrasound ribeye area, and ultrasound 12th-rib fat thickness; however, the two equations were not significantly different. Prediction equations for the cutability end points had similar R2 values whether live animal ultrasound measurements or actual carcass measurements were used. However, when ultrasound ribeye area was excluded from live animal predictions, lower R2 values were obtained for kilograms of product (0.81 vs 0.67) and percentages of product (0.41 vs 0.17). Conversely, the exclusion of ultrasound ribeye area had little effect on the prediction accuracy for kilograms of fat (0.75 vs 0.74) and percentage fat (0.50 vs 0.40). These data substantiate the ability of live animal ultrasound measures to accurately assess beef carcass composition and suggest that the alternative ultrasound measures, rump fat and gluteus medius depth, improve the accuracy of predicting fat-based carcass yields.     

Using ultrasound technology to predict pork carcass composition

Twenty market hogs were evaluated with real-time ultrasound both before and after slaughter. Fat measures (n = 9) were taken at various body locations along with the longissimus muscle area measurement at the 10th rib. After live ultrasound, the hogs were slaughtered and the unsplit carcasses were measured with ultrasound at the same live ultrasound locations. After chilling, carcass measures were taken at the same locations using a backfat probe for fat measures and a loin eye dot grid for measuring the longissimus muscle area. One side of each carcass was fabricated into the four lean cuts, which then were expressed as a percentage of the side weight. The most appropriate prediction equation found was a two-variable equation (fat thickness at the anterior tip of gluteus medius and longissimus muscle area) with a R2 of .83 and a RSD value of 1.67. This prediction equation was verified on a different sample of 20 market hogs; actual vs predicted four lean cuts revealed that the prediction equation had a R2 of .63 and a RSD value of 2.04. Although some accuracy and precision was lost when this live animal prediction equation was incorporated in market hog evaluation, this equation offers producers an objective mechanism for identifying carcass merit in live hogs.     

Prediction of swine carcass composition by total body electrical conductivity (TOBEC)

An experiment was conducted to determine prediction equations that used readings for total body electrical conductivity (TOBEC) in the model for estimation of total fat-free lean and total fat weight in the pork carcass. Ultrasound measurements of live hogs were used to select 32 gilts that represented a range in weight, muscling, and fatness. The TOBEC readings were recorded on warm carcass sides, chilled carcass sides, and the untrimmed ham from the left carcass side. Physical dissection and chemical analyses determined fat-free lean and fat weight of the carcass. All of the ham tissues were analyzed separately from the remainder of the carcass tissues to incorporate ham measurements for prediction of total fat-free lean and total fat weight in the entire carcass. Prediction equations were developed using stepwise regression procedures. An equation that used a warm carcass TOBEC reading in the model was determined to be the best warm TOBEC equation (R2 = 0.91; root mean square error = 0.81). A three-variable equation that used chilled carcass TOBEC reading, chilled carcass temperature, and carcass length in the model was determined to be the best chilled TOBEC equation (R2 = 0.93; root mean square error = 0.73). A four-variable equation that included chilled carcass side weight, untrimmed ham TOBEC reading, ham temperature, and fat thickness beneath the butt face of the ham in the model was determined to be the best equation overall (R2 = 0.95; root mean square error = 0.65). The TOBEC and the fat-free lean weight of the ham are excellent predictors of total carcass fat-free lean weight.     

Practical means for estimating pork carcass composition

Three hundred sixty-one market-weight barrow and gilt carcasses were physically dissected into bone, skin, fat and muscle. A three-variable multiple linear regression equation containing the same independent variables (warm carcass weight, 10th rib loin muscle area and 10th rib fat depth) used (U.S.) to determine pork carcass lean weight was found to be the most practical means for predicting weight of muscle standardized to 10% fat. Multiple linear regression equations containing more than three independent variables produced only slight improvements in R2 values; however, the standard deviation about the regression line was not greatly improved by the addition of more independent variables to this three-independent-variable regression model. A single multiple linear regression equation using the three independent variables above may not be adequate to describe variation over the entire live-weight range for all hogs marketed in the U.S. For most accurate muscle weight prediction, different equations should be used for weight subclasses with one equation for carcasses under 100 kg and another for those heavier than 100 kg. A single prediction equation for muscle weight was adequate for carcasses of both barrows and gilts.     

Using ultrasound measurements to predict body composition of yearling bulls

Carcass traits have been successfully used to determine body composition of steers. Body composition, in turn, has been used to predict energy content of ADG to compute feed requirements of individual animals fed in groups. This information is used in the Cornell value discovery system (CVDS) to predict DM required (DMR) for the observed animal performance. In this experiment, the prediction of individual DMR for the observed performance of group-fed yearling bulls was evaluated using energy content of gain, which was based on ultrasound measurements to estimate carcass traits and energy content of ADG. One hundred eighteen spring-born purebred and crossbred bulls (BW = 288 +/- 4.3 kg) were sorted visually into 3 marketing groups based on estimated days to reach USDA low Choice quality grade. The bulls were fed a common high-concentrate diet in 12 slatted-floor pens (9 to 10 head/pen). Ultrasound measurements including back-fat (uBF), rump fat, LM area (uLMA), and intramuscular fat were taken at approximately 1 yr of age. Carcass measurements including HCW, backfat over the 12th to 13th rib (BF), marbling score (MRB), and LM area (LMA) were collected for comparison with ultrasound data for predicting carcass composition. The 9th to 11th-rib section was removed and dissected into soft tissue and bone for determination of chemical composition, which was used to predict carcass fat and empty body fat (EBF). The predicted EBF averaged 23.7 +/- 4.0%. Multiple regression analysis indicated that carcass traits explained 72% of the variation in predicted EBF (EBF = 16.0583 + 5.6352 x BF + 0.01781 x HCW + 1.0486 x MRB - 0.1239 x LMA). Because carcass traits are not available on bulls intended for use as herd sires, another equation using predicted HCW (pHCW) and ultrasound measurements was developed (EBF = 39.9535 x uBF - 0.1384 x uLMA + 0.0867 x pHCW - 0.0897 x uBF x pHCW - 1.3690). This equation accounted for 62% of the variation in EBF. The use of an equation to predict EBF developed with steer composition data overpredicted the EBF predicted in these experiments (28.7 vs. 23.7%, respectively). In a validation study with 37 individually fed bulls, the use of the ultrasound-based equation in the CVDS to predict energy content of gain accounted for 60% of the variation in the observed efficiency of gain, with 1.5% bias, and identified 3 of the 4 most efficient bulls.     

Evaluation of the E+V video image analysis system as a predictor of pork carcass meat yield

This study was conducted to assess the ability of the VCS2001 (E+V, Oranienburg, Germany) video image analysis system to predict pork carcass composition. Pork carcasses (n = 278) were selected from a commercial packing plant to differ in weight, Fat-O-Meater (FOM) predicted percentage lean, and gender. Carcasses were imaged three times with the VCS2001, chilled overnight, and then sequentially fabricated into boneless subprimals. The VCS2001 accurately predicted the weight of total saleable product (R2 = 0.88, root mean square error [RMSE] = 1.84) and fat-corrected lean (R2 = 0.92, RMSE = 1.66), but autocorrelation existed between dependent and independent variables. The VCS2001 was acceptably accurate and precise in predicting weights of bone-in ham (R2 = 0.83, RMSE = 0.80), bone-in loin (R2 = 0.74, RMSE = 1.17), loin lean (R2 = 0.77, RMSE = 0.82), belly (R2 = 0.78, RMSE = 0.94), sparerib (R2 = 0.55, RMSE = 0.28), and boneless shoulder (R2 = 0.73, RMSE = 0.79). Weights were more accurately predicted than yields (as a percentage of hot carcass weight) of total saleable product (R2 = 0.47, RMSE = 1.97) or total fat-corrected lean (R2 = 0.44, RMSE = 1.89) using VCS2002, and it did not accurately predict percentages of bone-in ham (R2 = 0.45, RMSE = 1.13), ham lean (R2 = 0.32, RMSE = 1.46), bone-in loin (R2 = 0.29, RMSE = 1.36), loin lean (R2 = 0.56, RMSE = 0.90), belly (R2 = 0.43, RMSE = 1.08), sparerib (R2 = 0.08, RMSE = 0.32), or boneless shoulder (R2 = 0.30, RMSE = 0.88). New prediction models and equations were developed using VCS2001 output variables plus hot carcass weight to predict weight of total saleable product (R2 = 0.89, RMSE = 1.72) and fat-corrected lean (R2 = 0.93, RMSE = 1.55) with very minimal increases in accuracy and precision over that achieved using E+V-programmed models and equations. Use of new prediction models and equations marginally improved accuracy and precision of estimations of total saleable product yield (R2 = 0.56, RMSE = 1.81) and fat-corrected lean yield (R2 = 0.57, RMSE = 1.67) over that achieved using E+V-programmed models and equations. The VCS2001 was not able to predict pork carcass composition more accurately than existing technology; therefore, further development is needed to assure commercial viability of this instrument.     

Estimation in vivo of the body and carcass chemical composition of growing lambs by real-time ultrasonography

The relationship between ultrasound measurements and empty body and carcass chemical composition was investigated. A 500-V real-time ultrasound with a 7.5-MHz probe combined with image analysis was used to make in vivo measurements to predict the empty body and carcass chemical composition of 31 female lambs of two genotypes, ranging in BW from 18.2 to 48.9 kg. Eleven ultrasound measurements of s.c. fat, muscle, and tissue depth were taken at four different sites (over the 13th thoracic vertebra, between the 3rd and 4th lumbar vertebrae, at the 3rd sternebra of the sternum, and over the 11th rib, 16 cm from the dorsal midline). The single best predictor of empty body fat quantity and energy value was the s.c. fat depth over the 13th thoracic vertebra (r(2) = 0.904 and 0.912; P <0.01, respectively). Body weight was used with ultrasound measurements in multiple regression equations to establish the best independent variables combination for predicting chemical composition. Results showed that BW and two of the three ultrasound measurements (s.c. fat depth over the 13th thoracic vertebra, between the 3rd and 4th lumbar vertebrae, and tissue depth over the 11th rib, 16 cm from the dorsal midline), explained 94.7 to 98.7% (P < 0.01) of the quantity of water and fat and the energy value variation in the empty body and carcass. Body weight per se was the best predictor of the quantity of protein, accounting for 97.5 and 96.8% (P < 0.01) of the variation observed in the empty body and carcass, respectively. The results of this study suggest that BW and some ultrasound measurements combined with image analysis, particularly subcutaneous fat depth over the 13th thoracic vertebra, allow accurate prediction of empty body and carcass chemical composition in lambs.     

Evaluation of the effects of dietary fat, conjugated linoleic acid, and ractopamine on growth performance, pork quality, and fatty acid profiles in genetically lean gilts

An 8-wk study of the effects of CLA, rendered animal fats, and ractopamine, and their interactive effects on growth, fatty acid composition, and carcass quality of genetically lean pigs was conducted. Gilts (n = 228; initial BW of 59.1 kg) were assigned to a 2 x 2 x 3 factorial arrangement consisting of CLA, ractopamine, and fat treatments. The CLA treatment consisted of 1% CLA oil (CLA-60) or 1% soybean oil. Ractopamine levels were either 0 or 10 ppm. Fat treatments consisted of 0% added fat, 5% choice white grease (CWG), or 5% beef tallow (BT). The CLA and fat treatments were initiated at 59.1 kg of BW, 4 wk before the ractopamine treatments. The ractopamine treatments were imposed when the gilts reached a BW of 85.7 kg and lasted for the duration of the final 4 wk until carcass data were collected. Lipids from the belly, outer and inner layers of backfat, and LM were extracted and analyzed for fatty acid composition from 6 pigs per treatment at wk 4 and 8. Feeding CLA increased (P < 0.02) G:F during the final 4 wk. Pigs fed added fat as either CWG or BT exhibited decreased (P < 0.05) ADFI and increased (P < 0.01) G:F. Adding ractopamine to the diet increased (P < 0.01) ADG, G:F, and final BW. The predicted carcass lean percentage was increased (P < 0.05) in pigs fed CLA or ractopamine. Feeding either 5% fat or ractopamine increased (P < 0.05) carcass weight. Adding fat to the diets increased (P < 0.05) the 10th rib backfat depth but did not affect predicted percent lean. Bellies of gilts fed CLA were subjectively and objectively firmer (P < 0.01). Dietary CLA increased (P < 0.01) the concentration of saturated fatty acids and decreased (P < 0.01) the concentration of unsaturated fatty acids of the belly fat, both layers of backfat, and LM. Ractopamine decreased (P < 0.01) the i.m. fat content of the LM but had relatively little effect on the fatty acid profiles of the tissues compared with CLA. These results indicate that CLA, added fat, and ractopamine work mainly in an additive fashion to enhance pig growth and carcass quality. Furthermore, these results indicate that CLA results in more saturated fat throughout the carcass.     

Genotype and treatment biases in estimation of carcass lean of swine.

Carcasses of 181 barrows, representing five genotypes, 1) H x HD, 2) SYN, 3) HD x L[YD], 4) L x YD, and 5) Y x L (H = Hampshire, D = Duroc, SYN = synthetic terminal sire line, L = Landrace, and Y = Yorkshire), and two levels of ractopamine (RAC) treatment (0 and 20 ppm) were completely dissected and the data were used to examine genotype and treatment (RAC) biases in estimation of fat-standardized lean weight and to evaluate accuracies and precisions realized by use of equations based on variables derived from different technologies. Independent variables used to establish regression equations represented technologies of direct carcass measurements, optical probe data, TOBEC (total body electrical conductivity) readings, and dissected (DHMLN) and fat-standardized (FSHMLN) ham lean. Genotype bias existed when any equation from a single technology was used and was minimized by combining FSHMLN with one TOBEC reading, carcass length, and the probe measurement of 10th rib fat depth. Large RAC biases appeared when equations from direct carcass measurements or optical probe data were used and were minimized by an equation using either DHMLN or FSHMLN. A practical equation with relatively high R2 value and small genotype and RAC biases were developed by combining TOBEC readings with direct carcass measurements of 10th rib fat depth and warm carcass weight.     

Effects of L-carnitine supplementation on quality characteristics of fresh pork bellies from pigs fed 3 levels of corn oil

Crossbred pigs (n = 216) were used to test the effect of supplemental L-carnitine (CARN) on the fatty acid composition and quality characteristics of fresh pork bellies from pigs fed diets formulated with different inclusion levels of corn oil. Pigs were blocked by BW (43.6 ± 1.0 kg) and allotted randomly to pens of 6 pigs within blocks. Then, within blocks, pens were assigned randomly to 1 of 6 dietary treatments in a 2 × 3 factorial arrangement, with either 0 or 100 mg/kg of supplemental CARN and 3 dietary inclusion levels (0, 2, or 4%) of corn oil (CO). When the lightest block weighed 125.0 kg, all pigs were slaughtered, and left-side bellies were captured during carcass fabrication for quality data collection. Fresh pork bellies were evaluated for length, width, thickness, and firmness (bar-suspension and Instron-compression methods) before a 2.5-cm-wide strip of belly was removed and subsequently dissected into subcutaneous fat, primary lean (latissimus dorsi), secondary lean (cutaneous trunci), and intermuscular fat for fatty acid composition determination. Although belly length, width, and thickness of fresh pork bellies were not affected by CARN (P ≥ 0.128) or CO (P ≥ 0.073), belly firmness decreased linearly (P < 0.001) with increasing dietary CO, but there was no (P ≥ 0.137) effect of CARN on any belly firmness measure. Dietary CARN increased (P < 0.05) the proportion of total SFA in the intermuscular fat layer, increased (P < 0.05) the proportion of total MUFA in the primary and secondary lean layers, and decreased (P < 0.05) the proportion of total PUFA in the intermuscular fat and secondary lean layers of pork bellies. Moreover, the SFA and MUFA compositions decreased linearly (P < 0.001) with increasing dietary CO, and the rate of the decrease in SFA composition was greater (P < 0.001) in the fat layers than the lean layers. Conversely, the PUFA content increased linearly (P < 0.001) with increasing dietary CO, and the rate of the increase in PUFA was greater (P < 0.001) in the fat than the lean layers, and greater (P = 0.022) in the primary than secondary lean layer. Results from this study would indicate that differences in the amount and rate of fatty acid deposition associated with feeding increased amounts of CO, along with moisture differences among the belly layers, combine to negatively affect fresh pork belly firmness.     

Phenotypic and genetic variation in body weight,food intake and energy utilisation in Hereford cattle I. Performance test results

From body weight, food intake and carcass composition data on 542 Hereford bull calves, measuredfrom 200 to 400 days, several traits relating to the efficiency of beef cattle production were derived and analysed. Traits included body weight at various ages, weight gain, predicted carcass lean content, lean growth rate, food intake, food conversion ratio, lean food conversion ratio, food intake in relation to metabolic body weight, energy required for protein and fat deposition, and predicted maintenance expenditure.Maintenance expenditure and the costs of fat and protein deposition were calculated by two means,firstly from allometric equations describing fat and protein accretion, and secondly from a multiple regression of food intake on weight gain and predicted carcass lean content. The two methods gave different mean values, but the correlations between traits calculated by the two methods were almost all 1.00. Exponents for metabolic body weight derived from the two methods were 0.738 and 0.758, respectively.Genetic parameters were calculated using multivariate Restricted Maximum Likelihood techniques.Body weight, carcass composition and traits combining these measurements were moderately to strongly inherited whereas traits related to food intake and efficiency were weakly to moderately inherited. Energy used to deposit fat and lean was more strongly inherited than predicted maintenance expenditure, and these traits were genetically almost uncorrelated. Maintenance energy expenditure showed no genetic relationship with predicted carcass lean content. Efficiency and predicted maintenance expenditure were favourably correlated.     

Prediction of retail product and trimmable fat yields from the four primal cuts in beef cattle using ultrasound or carcass data

The most widely used system to predict percentage of retail product from the four primal cuts of beef is USDA yield grade. The purpose of this study was to determine whether routine ultrasound measurements and additional rump measurements could be used in place of the carcass measurements used in the USDA yield grade equation to more accurately predict the percentage of saleable product from the four primals. This study used market cattle (n = 466) consisting of Angus bulls, Angus steers, and crossbred steers. Live animal ultrasound measures collected within 7 d of slaughter were 1) scan weight (SCANWT); 2) 12th- to 13th-rib s.c. fat thickness (UFAT); 3) 12th- to 13th-rib LM area (ULMA); 4) s.c. fat thickness over the termination of the biceps femoris in the rump (URFAT; reference point); 5) depth of gluteus medius under the reference point (URDEPTH); and 6) area of gluteus medius anterior to the reference point (URAREA). Traditional carcass measures collected included 1) HCW; 2) 12th-to 13th-rib s.c. fat thickness (CFAT); 3) 12th- to 13th-rib LM area (CLMA); and 4) estimated percentage of kidney, pelvic, and heart fat (CKPH). Right sides of carcasses were fabricated into subprimal cuts, lean trim, fat, and bone. Weights of each component were recorded, and percentage of retail product from the four primals was expressed as a percentage of side weight. A stepwise regression was performed using data from cattle (n = 328) to develop models to predict percentage of retail product from the four primals based on carcass measures or ultrasound measures, and comparisons were made between the models. The most accurate carcass prediction equation included CFAT, CKPH, and CLMA (R2 = 0.308), whereas the most accurate live prediction equation included UFAT, ULMA, SCANWT, and URAREA (R2 = 0.454). When these equations were applied to a validation set of cattle (n = 138), the carcass equation showed R2 = 0.350, whereas the ultrasound data showed R2 = 0.460. Ultrasound measures in the live animal were potentially more accurate predictors of retail product than measures collected on the carcass.     

猪胴体在线瘦肉率回归预测研究

选购不同类型的商品猪94头,分别测其瘦肉率、胴体重、背膘厚度等指标,并以瘦肉率为因变量,其它指标为自变量,采用SAS8.0软件进行数据分析,建立了不同商品猪胴体瘦肉率最优回归方程,并对回归方程进行了方差分析及残差散点图的回归诊断,结果表明只采用胴体重和背膘厚度两个指标所得的胴体瘦肉率预测方程具有较高的准确度和精度度。     

Evaluation of alternative techniques to determine pork carcass value.

Three techniques for estimating the value of pork carcasses were evaluated: an optical probe, a real-time ultrasound scanner, and an electromagnetic scanner (EMSCAN). The ability of these techniques to predict carcass value was compared to the predictive ability of actual measures of backfat depth and longissimus muscle area taken with a ruler and a dot grid. Results indicated the EMSCAN model was the best predictor of carcass value. However, the optical probe, ultrasound, and the ruler/dot grid all provided information not contained in the EMSCAN model. The choice among ultrasound, the optical probe, and the ruler/dot grid depends on how the carcass will be used. There is no significant difference between ultrasound and the ruler/dot grid or the optical probe and the ruler/dot grid if the carcass is to be marketed in wholesale primal form, but the ruler/dot grid is superior if the ham and loin are to be sold as lean, boneless products. A model combining the EMSCAN and optical probe readings provided more accurate value predictions than either technique alone. A carcass value matrix for use in pricing pork carcasses was developed using readings from the optical probe. Carcass use has a substantial impact on value differences between fat and lean pigs.