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

The objective of this study was to test and validate electromagnetic scanning of whole pork carcasses in an on-line, integrated, industrial configuration. The electromagnetic (EM) scanner was installed in two pork processing facilities (Plant A and Plant B). Plant A was a small pork fabrication plant that further processed chilled pork carcasses. Carcasses were delivered to Plant A by refrigerated trucks. The amount of EM energy absorbed by the carcasses was recorded as they were conveyed through the EM field. A plot of the absorption units over time (EM scan curve) was used to obtain predictive variables for estimating carcass and primal cut composition. Forty-eight whole, chilled carcasses (Group A) were electromagnetically scanned and conveyed onto the fabrication line. The average percentage carcass lean for Group A was 49.1% (range = 36.5 to 59.5%). Right carcass sides were removed from the processing line, fabricated into primal cuts, and dissected into fat, lean, and bone. Prediction equations were developed from EM scans for weight of total dissected carcass lean (R2 = .830; root mean square error = 1.80 kg), percentage of carcass lean (R2 = .820; root mean square error = 2.29%), and weight of dissected ham, longissimus muscle, and shoulder lean. In Plant B, the electromagnetic scanner was installed at the end of a pork slaughter line to ensure carcass scanning at a consistent carcass temperature. Fifty whole, pre-rigor eviscerated carcasses (Group B) were electromagnetically scanned before entering the chill cooler where fat and loin tissue depths were obtained by an optical grading probe. The average percentage carcass lean for Group B was 46.7% (range = 30.1 to 57.3%). Prediction equations were developed from EM scans for weight of total dissected carcass lean (R2 = .904; root mean square error = 1.59 kg), percentage of carcass lean (R2 = .863; root mean square error = 2.05%), and weight of dissected ham, loin, and shoulder lean. Statistical equations developed for the prediction of dissected primal cut lean were superior from EM scans of Group B (prerigor) carcasses. Electromagnetic scanning proved more statistically efficient than optical probes for predicting weight of dissected carcass lean and percentage of carcass lean. Statistical comparison of EM scan equations from Groups A and B are not completely valid because two different populations of carcasses were tested at different times of the year. The results of this study show that EM scanning has the potential to accurately predict pork carcass composition in a fully automated, on-line industrial configuration.

Pork carcass composition derived from a neural network model of electromagnetic scans.

We used an advanced computer logic system (NETS 3.0) to decipher electromagnetic (EM) scans in lieu of traditional linear regression for estimation of pork carcass composition. Fifty EM scans of pork carcasses were obtained on-line (prerigor) at a swine slaughter facility. Right sides were cut into wholesale parts and dissected into fat, lean, and bone to obtain total dissected carcass and primal cut lean. In this study, the input layer consisted of 81 nodes (80-point EM scan curve and warm carcass weight), one hidden layer of 42 nodes, and an output layer consisting of one node, which were run separately for outputs of ham, loin, or shoulder lean. The hidden layer connected to the output of total lean contained 50 nodes. Thirty-five scans were used for training of the network. The new network was then tested with 15 previously unseen input/output pairs. Separate neural networks were developed for the estimation of dissected total carcass, ham, loin, and shoulder lean. The NETS configuration improved on linear regression equations for estimation of total carcass lean by .31 kg, ham lean by .284 kg, and shoulder lean by .148 kg. Our results show that advanced computer logic systems have the capacity to improve upon traditional linear regression equations for prediction of pork carcass composition.

Palatability of prerigor cooked boar meat.

Cooking reduces odor intensity in boar meat but also may induce lipid oxidation unless the meat pH is above approximately 6.0. This research was designed to determine the feasibility of cooking boar meat in the prerigor state to overcome boar odor and lipid oxidation problems. Prerigor and postrigor triceps brachii muscle samples from 40 boars (20 Duroc and 20 Yorkshire) were cooked to 60 degrees C, frozen and stored at -20 degrees C, reheated in a 60 degrees C water bath for 1 h, and then subjected to pH, thiobarbituric acid (TBA), and sensory analyses. Boar odor intensity and skatole concentration in backfat samples were determined by olfactory test and HPLC, respectively. Cooked (initial cooking) prerigor meat was found to have higher (P < .05) pH and lower (P < .05) TBA values than comparable postrigor meat (6.44 vs 6.09 and 2.15 vs 3.23, respectively). Regression analysis indicated an inverse relationship between pH and TBA values (r = -.52; P < .01). No appreciable changes in TBA values were noted after frozen storage for 14 to 98 d, but reheating increased TBA values (P < .05) in both prerigor and postrigor samples (3.45 vs 4.32, respectively). Sensory evaluation scores indicated that prerigor cooked meat was less tender with more pronounced rancid flavor than postrigor cooked meat (P < .05), but panelists may have allowed the toughness of the prerigor samples to adversely affect their flavor scores. No difference in boar odor was detected between rigor states or breeds. Mean skatole concentration in backfat was .12 micrograms/g and no difference was detected between breeds.(ABSTRACT TRUNCATED AT 250 WORDS)

Effects of ractopamine, genotype, and growth phase on finishing performance and carcass value in swine: I. Growth performance and carcass merit.

A replicated factorial experiment using 183 individually fed crossbred barrows was conducted. Pigs were fed an 18.5% CP (.95% lysine) diet with 3,594 kcal of ME/kg. The effects of five genotypes (GT): 1) Hampshire (H) x (H x Duroc [D]), 2) synthetic terminal sire line, 3) (H x D) x (Landrace [L] x [Yorkshire (Y) x D]), 4) L x (Y x D), and 5) Y x L; two levels of ractopamine (RAC) treatment: 0 and 20 ppm; and three treatment weight periods (WT): 1) 59 to 100, 2) 73 to 114, and 3) 86 to 127 kg live weight on growth performance and carcass traits were evaluated. Ractopamine increased (P less than .0001) amount (FSL) and percentage (PFSL) of carcass lean standardized to 10% fat content, loin eye area (LEA), and dressing percentage (PDRES) and reduced (P less than .01) amount (DF) and percentage (PDF) of dissected fat. Magnitudes of RAC effects were smaller than those reported by other researchers. Effects of GT and WT on all growth and carcass traits were highly significant (P less than .001) except for those of WT on ADG (P less than .05) and GT on average feed intake (AFI) and backfat thickness at the last rib (BFLR; P less than .05). Genotype 3 performed better for most economically important traits than did GT 1 and 4, suggesting that heterosis existed in GT 3, which essentially was obtained by crossing GT 1 and 4. Among the three treatment periods, WT 2 had the highest ADG. As BW increased from WT 1 to 3, AFI and AFI/ADG ratio (FCR) increased and lean percentage decreased.(ABSTRACT TRUNCATED AT 250 WORDS)

Effects of ractopamine, genotype, and growth phase on finishing performance and carcass value in swine: II. Estimation of lean growth rate and lean feed efficiency.

A replicated factorial experiment using 183 individually fed crossbred barrows was conducted. The pigs were fed an 18.5% CP (.95% lysine) diet with 3.594 kcal of ME/kg. The effects of five genotypes (GT): 1) Hampshire (H) X (H X Duroc [D]), 2) synthetic terminal sire line, 3) (H X D) X (Landrace [L] X [Yorkshire (Y) X D]), 4) L X (Y X D), and 5) Y X L; two levels of ractopamine (RAC) treatment: 0 and 20 ppm; and three treatment weight periods (WT): 1) 59 to 100, 2) 73 to 114, and 3) 86 to 127 kg live weight on ADG of dissected lean (ADLG) and fat standardized lean adjusted to 10% fat content (ADSLG) and feed efficiency of ADLG (LFE) and ADSLG (SLFE) were evaluated. Initial carcass lean quantity of each individual animal was determined by a regression equation (R2 = .95) generated from 30 additional barrows (six per GT) slaughtered at 59 kg and 30 (six per GT) untreated pigs slaughtered at 100 kg average live weight. Logarithmic and reciprocal transformations of dependent variables were used to stabilize heterogeneous variances and to improve normality of the residuals. Ractopamine increased (P less than .0001) ADLG, ADSLG, LFE, and SLFE, respectively, by 19.5, 25.0, 19.6, and 25.5%. Differences (P less than .001) were observed among genotypes for all traits, showing that considerable variation existed in the data and indicating that genetic improvement can be realized through the identification and selection of superior genotypes.(ABSTRACT TRUNCATED AT 250 WORDS)

Electromagnetic scanning to predict lamb carcass composition.

The electromagnetic scanner generates a constant, low-level electromagnetic field (2.5 MHz) within a large plexiglass tube. The amount of electromagnetic (EM) energy transferred (to the carcass) is highly related to lean tissue. A plot of the absorption units over distance can be used to assess the total mass of lean tissue and of the respective primal cuts. The difference in curve height between two points (D), peak phase absorption, and linear carcass measurements (pre-rigor, HCWT or post-rigor, CWT carcass weight, and carcass length, LENG) were used to predict total dissected lean (TOTLEAN), dissected leg lean (LEGLEAN), and percentage of dissected carcass lean (PERLEAN). Twenty-one pre-rigor and 22 post-rigor (24 h chill) lamb carcasses, average weight 26.8 (+/- 4.2 kg) and 26.4 (+/- 4.1 kg) kg, respectively, were evaluated from measurements of total body electrical conductivity (TOBEC). Two geometric orientations were tested for statistical accuracy in this study: A) each carcass entered the EM tunnel rear leg first, on its left lateral side, neck facing the right side of the tunnel; and B) each carcass entered the EM tunnel rear leg first, breast down, and neck up. Orientation A proved more statistically efficient for pre-rigor carcasses, and orientation B was more desirable for post-rigor carcasses. Multiple-regression models involving HCWT, LENG, and a single D measure accounted for 98.0 and 95.0%, respectively, of the total variation in pre-rigor carcass TOTLEAN and LEGLEAN in A.(ABSTRACT TRUNCATED AT 250 WORDS)

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.

Effect of heating rate on palatability and associated properties of pre- and postrigor muscle.

Precooked, uncured meat is not widely available to consumers, partially because of associated palatability problems and lack of published information on heat uptake under different industrial conditions. The objectives of this study were to determine the tenderness, extent of lipid oxidation, and total cooking losses in pre- and posterior beef and pork roasts heated at different rates. The muscles were cooked in stainless-steel, perforated heating chambers at oven temperatures of 150, 200, or 250 degrees C and the temperature rise during and after heating was monitored with a digital temperature recorder. Samples were vacuum-packaged, frozen at -20 degrees C for 45 d, thawed at 4 degrees C for 24 h, and reheated in 60 degrees C water for 1 h. Cooking losses, Warner-Bratzler shear force values, thiobarbituric acid values, and pH were determined. The results provide heating curves for pre- and postrigor beef and pork roasts at three oven temperatures. Prerigor samples of both species were less tender than postrigor samples (P < .05). Cooking losses were generally low in prerigor samples of both species compared with postrigor samples (P < .05). All beef samples had relatively low thiobarbituric acid (TBA) values before and after storage, whereas pork samples had relatively high TBA values before and after storage. Results indicate that prerigor cooked roasts shrink less, are equivalent or better in oxidative stability, and are less tender than postrigor cooked roasts under the conditions of this experiment.

Electrical measurement for detecting early postmortem changes in porcine muscle.

Use of electrical measurements to detect quality defects in porcine muscle in the early postmortem period was evaluated. Justification for use of a tetrapolar, constant current electrode configuration instead of bipolar electrodes was provided for measurements at low frequencies. Interrelationships among electrical properties, pH values, ATP decline, temperature, time postmortem, and final water-holding capacity (WHC) of porcine muscle were quantified using 25 hogs. Immediately after exsanguination, a section of the left longissimus muscle (LM) was excised to obtain rigor shortening patterns and complex impedance measurements over a 10-h period at 37 degrees C. Complex impedance measurements were taken using a tetrapolar electrode configuration at 1 kHz and .156 mA. At 15, 45, and 90 min postmortem, pH, ATP/IMP absorbance (R), and conductivity measured by the Tecpro Pork Quality Meter (PQM) were measured on the right side LM. At 24 h postmortem, WHC, pH, R, PQM, Hunter Color Lab values, and subjective quality scores were evaluated on the left LM. The WHC measurements were used to group carcasses into normal (n = 17) and abnormal (n = 8) categories. Mean pH and R at 45 and 90 min were different (P < .05) but pH at 24 h was not different between the normal and abnormal groups. Onset and completion of rigor were more rapid in carcasses with low WHC (P < .05). The PQM values were greater (P < .05) in the abnormal group at 90 min and 24 h postmortem. Excised muscle measurements of relative impedance (Z*) and phase (theta*) showed Z* and theta* increased more rapidly within the first 15 min postmortem (P < .1) for samples with abnormal WHC. However, one PSE carcass showed an immediate rapid decrease in Z* and theta*. Results suggest measurement of rate of change of impedance and phase angle before 90 min postmortem would be a better prediction of ultimate quality than absolute magnitude of impedance.

Assessment of lamb carcass composition from live animal measurement of bioelectrical impedance or ultrasonic tissue depths.

Market weight lambs, average weight 52.5 kg (+/-6.1), were used to evaluate nontraditional live animal measurements as predictors of carcass composition. The sample population (n = 106) represented U.S. market lambs and transcended geographic location, breed, carcass weight, yield grade, and production system. Realtime ultrasonic (RU) measurements and bioelectrical impedance analysis (BIA) were used for development and evaluation of prediction equations for % boneless, closely trimmed primal cuts (BCTPC), weight or % of dissected lean tissue (TDL), and chemically derived weight or % fat-free lean (FFL). Longitudinal ultrasonic images were obtained parallel to the longissimus thoracis et lumborum (LTL), positioning the last costae in the center of the transducer head. Images were saved and fat and LTL depths were derived from printed images of the ultrasonic scans. Bioelectrical impedance analysis was administered via a four-terminal impedance plethysmograph operating at 800 microA at 50 kHz. Impedance measurements of whole-body resistance and reactance were recorded. Prediction equations including common linear measurements of live weight, heart girth, hindsaddle length, and shoulder height were also evaluated. All measurements were taken just before slaughter. Bioelectrical impedance measurements (as compared to RU and linear measurements) provided equations for %BCTPC, TDL, %TDL, FFL and %FFL with the highest R2 and lowest root mean square error. Even though BIA provided the best equations of the three methodologies tested, prediction of proportional yield (%BCTPC, %TDL, and %FFL) was marginal (R2 = .296, .551, and .551, respectively). Equations combining BIA, RU, and linear measurements greatly improved equations for prediction of proportional lean yield.

Evaluation of alternative measures of pork carcass composition.

Carcass and live measurements of 203 pigs representing seven genetic populations and four target live weights (100, 114, 128, and 152 kg) were used to evaluate alternative measures of carcass composition. Measures of carcass lean (fat tissue-free lean, FFLM; lipid-free soft tissue, LFSTIS; and dissected lean in the four lean cuts, DL), fat (total carcass fat tissue, TOFAT), and lipid mass (soft tissue lipid, STLIP) were evaluated. Overall, LFSTIS was 22.8% greater than FFLM (47.8 vs 38.9 kg) and TOFAT was 30% greater than STLIP (38.5 vs 29.6 kg). The allometric growth coefficients relative to carcass weight were different for the measures: b = 0.776, 0.828, 0.794, 1.37, and 1.49 for FFLM, LFSTIS, DL, TOFAT, and STLIP, respectively. At 90 kg carcass weight, the predicted growth of FFLM, LFSTIS, TOFAT, and STLIP was 0.314, 0.420, 0.553, and 0.446 kg/kg increase in carcass weight. The difference between FFLM and LFSTIS, representing nonlipid components of the carcass fat tissue, was greater for barrows than for gilts (9.2 vs 8.6 kg). Lipid-free soft tissue mass was predicted more accurately from carcass or live animal measurements than FFLM with smaller relative RSD (4.6 vs 6.5% of their mean values). The alternative measures of carcass composition were evaluated as predictors of empty body protein (MTPRO) and lipid (MTLIP) mass. Empty body protein was predicted with similar accuracy (R2 = 0.74 to 0.81) from either DL, FFLM, LFSTIS, or ribbed carcass measurements. Empty body lipid was predicted more accurately from TOFAT (R2 = 0.92) or STLIP (R2 = 0.93) than ribbed carcass measurements (R2 = 0.88). Although the alternative measures of lean mass (LFSTIS vs FFLM) and lipid mass (TOFAT vs STLIP) were highly related to each other (r = 0.93 to 0.98), they had different relative growth rates (allometric coefficients) and thus cannot be predicted as linear functions of the similar alternative variable without significant weight group biases. From the 100- to 152-kg target weight groups, gilts gained 12.9% greater FFLM and 12.1% greater MTPRO but only 4.4% greater LFSTIS than barrows. Fat-free lean mass is more precise as a measure of muscle growth and as a predictor of lysine requirements. Lipid-free soft tissue can be obtained more quickly and predicted more accurately from carcass or live animal measurements.

Assessment of fresh pork color with color machine vision.

Currently, fresh pork color is visually evaluated using either the Japanese Pork Color Standards (JPCS) or the National Pork Producers Council Pork Quality Standards (NPPC) as a reference. Although useful, visual evaluation of meat color can vary with evaluator and may be quite expensive. In this study, three separate studies were used to compare the ability of color machine vision (CMV) and untrained panelists to evaluate pork color. Panels visually evaluated over 200 pork loin chops using either the JPCS or NPPC reference standards. Results from each panel were used to evaluate the ability of the CMV to sort pork loin chops based on the same criteria. Representative samples, typical of each color class, were used to train neural-network-based image processing software. After training, the CMV system was used to evaluate quality classes of pork samples based on color distribution. Classification by CMV was compared with the average panel score, rounded to the nearest integer. Training the CMV system using images of actual meat samples resulted in a stronger correlation to panel scores than training with either set of artificial color standards. Agreement between the CMV system and the panels was as high as 90%. Agreement between individual panelists and the integer panel average (52 to 85%) was less than that observed for CMV classification. Finally, the on-line performance of CMV using a laboratory conveyor system was simulated by repeatedly classifying 37 samples at a speed of 1 sample per second. Collectively, these results demonstrate that CMV is a rapid and repeatable means of evaluating pork color.

Analysis of body composition changes of swine during growth and development.

This study was conducted to model the growth of carcass, viscera, and empty body components and component composition of pigs. Quantitative tissue and chemical composition of 319 swine, representative of barrows and gilts from five commercial genetic populations, was determined at eight stages of growth between 25 and 152 kg. After whole body grinding and carcass dissection, proximate analyses were performed to calculate concentrations of protein, lipid, moisture, and ash of carcass, viscera, empty body, carcass lean, and carcass fat. Linear and nonlinear equations were developed to investigate the growth patterns of each component. Nonlinear growth functions accounted for the greatest amount of variation in empty body protein, lipid, moisture, and ash mass. Differences (P < .05) existed between barrows and gilts for nearly all components investigated. Carcass lean and fat tissues significantly increased in lipid percentage and decreased in moisture percentage as live weight increased. There were significant changes in the ratio and composition of the tissues of barrows and gilts during growth. Nonlinear models fitted the data better than allometric equations for nearly all of the components investigated.

Evaluation of electronic technology to assess lamb carcass composition.

Accurate price signals are essential for producers of American lamb to ensure production of uniformly lean animals. Development of carcass merit-pricing systems will require the use of objective technology for assessing carcass composition or lean distribution. The objective of this study was to evaluate electronic technologies for accurate determination of lamb carcass composition. Lambs (n = 106) were selected as a representation of U.S. market lambs that transcended geographic location, sex, breed, carcass weight, yield grade, and production system. The independent variables used to predict lamb composition varied with the technology. The electronic technologies tested included realtime ultrasound, optical reflectance probe, bioelectrical impedance analysis, and electromagnetic scanning (TOBEC). All technologies, except realtime ultrasound, were tested on warm (prerigor) carcasses and repeated after a 24-h chill. Longitudinal ultrasonic scans of fat and muscle tissue depth and grading probe fat depths were marginal predictors of proportional carcass yield. The TOBEC measurements often accounted for more variability associated with kilograms of dissected lean and percentage of carcass lean than did carcass weight. Equations from TOBEC measurements were the most accurate predictors of weight and percentage of dissected and fat-free lean. Bioelectrical impedance measurements of resistance and reactance combined with carcass weight were also good predictors of carcass composition. Prediction of carcass lean distribution by measures of TOBEC were the most accurate for prediction of leg lean. The implications of usefulness of these technologies will depend on the commitment of the U. S. sheep industry in development of a lamb price discovery system based on carcass composition.

Biases associated with genotype and sex in prediction of fat-free lean mass and carcass value in hogs.

Carcass and live measurements of 165 market hogs that represented seven genotypes were used to investigate genotype and sex biases associated with the prediction of fat-free lean mass (FFLM) and carcass value. Carcass value was determined as the sum of the product of weight of individual cuts and their average unit prices adjusted for slaughter and processing costs. Independent variables used in the prediction equations included carcass measurements, such as optical probe, midline ruler, ribbed carcass measurements, and electromagnetic scanning (EMSCAN), and live animal ultrasonic scanning. The effect of including subpopulation mean values of independent variables in the prediction equations for FFLM and carcass value was also investigated. Genotype and sex biases were found in equations in which midline backfat, ribbed carcass, EMSCAN, and live ultrasonic scanning were used as single technology sets of measurements. The prediction equations generally undervalued genotypes with above-average carcass value. Biases were reduced when measurements of combined technologies and mean adjusted variables were used. The FFLM and carcass value of gilts were underestimated, and they were overestimated of barrows. Equations that combined OP and EMSCAN technologies were the most accurate and least biased for both FFLM and carcass value. Equations that included carcass weight and midline last-rib backfat thickness measurements were the least accurate and most biased. Genotype and sex biases must be considered when predicting FFLM and carcass value.

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.

Preparation of muscle samples for comparative electron microscopy.

The interpretation of muscle structure by scanning electron microscopy (SEM) has not been consistent among various studies. Consequently, the literature is confusing with respect to the identity of T-tubules, transverse ridges, Z-disks, and intermyofibrillar connections. The objective of this research was to evaluate the effects of different methods of sample preparation and imaging on ultrastructural details of previously identified transverse structures and intermyofibrillar connections and to verify or disprove the commonality of these structures under different viewing conditions. Scanning electron microscopy coupled with a cold stage, SEM at room temperature, and transmission electron microscopy (TEM) of thin sections were most appropriate for exposing detail of inter- and intracellular structures and for measuring sarcomere length and spacing of intermyofibrillar connections. Scanning electron microscopy of samples mounted on a cold stage, fractured, and sublimed provided excellent images of meat and muscle ultrastructure and may be used in correlative microscopy. Sarcomere length and spacing between intermyofibrillar connections were similar among most specimen preparation techniques and were affected similarly by heat treatments. Results indicate that the regularly spaced transverse structures viewed by conventional SEM and the intermyofibrillar connections viewed by low-temperature SEM are Z-disks.

Electromagnetic scanning of beef quarters to predict carcass and primal lean content.

To study the use of electromagnetic scanning in prediction of lean content in beef carcasses and cuts, 100 beef cattle (60 steers and 40 heifers), representing a broad range in external fat thickness (.1 to 2.9 cm) and live weight (414 to 742 kg), were selected. Chilled right sides were divided into streamlined (foreshank, brisket, and ventral plate removed) forequarters (FQ) and full hindquarters (HQ) and scanned. Primal rounds, loins, ribs, and chucks were fabricated from the right side, scanned, and physically separated into lean, fat, and bone. Prediction equations for dissected lean content and percentage of lean included the peak of the electromagnetic scan response curve (obtained from scanning the HQ or FQ), length, temperature and weight of the scanned cut, and fat thickness at the 12th rib. Using the coefficient of determination, root mean square error, and Mallows' Cp statistic, the best model for each dependent variable (weight and percentage of lean) that included up to five independent variables was selected. Prediction equations for the HQ or FQ of steers accounted for 84 to 93% of the variation in lean weight of beef sides and quarters and 71 to 93% of primals. Sixty-one to 75% of the variation in percentage of lean in sides and quarters and 48 to 65% of primals was also explained. Similar results were obtained for heifer carcasses. Predicting percentage of lean in any scanned cut, rather than weight of lean, accounted for less of the variation. Weight and fat thickness contributed significantly when predicting percentage of lean.(ABSTRACT TRUNCATED AT 250 WORDS)

Alternative pork carcass evaluation techniques: I. Differences in predictions of value.

Dissected and predicted wholesale and lean boneless values for 154 pork carcasses representing seven genotypes with substantial variation in carcass composition and percentage of lean were determined. Dissected carcass value was determined using a component pricing model, and four alternative models were specified to predict that value. The models included measurements from a ruler (RULER) and two carcass evaluation technologies, Hennessy probe (PROBE) and electromagnetic scanner (EMS1). A combination of the PROBE and EMS1 models (EMS2) was also used. For wholesale value, R2 were .40, .70, .59, and .74, and the RSD were 8.18, 5.77, 6.76, and 5.38 ($/100 kg of carcass value) for RULER, PROBE, EMS1, and EMS2, respectively. For lean boneless value, the R2 were .41, .73, .59, and .74, and the RSD were 8.34, 5.67, 6.99, and 5.51 ($/100 kg of carcass value) for RULER, PROBE, EMS1, and EMS2, respectively. The results indicate that a combination of probe and electromagnetic scanner measurements provided the best fit to dissected value.

Alternative pork carcass evaluation techniques: II. Statistical analysis of error attributable to sex, genotype, and weight.

Carcasses of 154 hogs representing seven genotypes with substantial variation in carcass composition and percentage of lean were completely dissected and analyzed. Measurements from a ruler, Hennessy probe, and electromagnetic scanner were each used to predict wholesale and lean boneless carcass value. Error, defined as dissected value minus predicted value, due to the omission of sex, genotype, weight, and their interactions was estimated for each model. The errors were significantly different from zero for the models using ruler and electromagnetic scanning measurements separately (P < .01). Errors due to sex, genotype, weight, and their interactions were greatest for the less lean barrows. A combination of probe and electromagnetic scanner measurements resulted in the least error. The value of barrows with low percentage of lean was consistently overpredicted, whereas the value of leaner gilts was underpredicted for the models using ruler and electromagnetic scanning separately (P < .001).