Carcase grading reflects the variation in beef yield - a multivariate method for exploring the relationship between beef yield and carcase traits.

Beef carcases in Europe are classified as a proxy for the quantity and ratio of tissues, commonly referred to as yield. It is important that proxies accurately measure yield as they contribute to financial transactions between abattoirs and producers. The main purpose of the study was therefore to examine the ability of EUROP carcase classification to explain the variation in yield. Furthermore, the effect of breed, as a confounder, was also examined. A multivariate definition of yield separating the carcase into six product categories was utilised as a response in a linear regression analysis. The conclusion was that EUROP and carcase features explain the majority of yield variation. Breed has an effect on yield beyond what is explained by carcase features including classification. The magnitude of the breed effects varies with breed and product category.

A CT-image based pig atlas model and its potential applications in the meat industry.

In this communication we present a novel pig atlas model which is represented by a parametric linear Lagrange or cubic Hermite mesh. The model is developed from data points digitized from a 3D pig CT image. In total 84 muscles and 121 bones are included in the atlas, representing the tissue structures most relevant to the industry. We discuss its potential applications in virtual meat cuts and statistical shape analysis for pig breeding and genetics companies.

The genetic correlation between scapula shape and shoulder lesions in sows.

Shoulder lesions and body condition of sows at weaning have both environmental and genetic causes. The traits can be scored at farm level, and following recording, the traits can be included in the breeding goal and directional selection can be applied. However, to further increase the genetic progress of these traits, it is advantageous to develop indicator traits on the selection candidates (test boars or gilts, not yet exhibiting the phenotype themselves). It has previously been suggested that the scapula morphology and the spine of scapula might be a key factor for the sow to develop shoulder lesions. In this study, we developed 11 novel traits describing the morphology of the shoulder blade based on computed tomography images from scanned test boars. These traits include the area, length, width, height, and volume of the shoulder blade as well as 6 traits obtained from principal component analysis, describing 80% of the variation observed for the scapula spine profile. The analyzed traits have moderate to high heritability (h2 from 0.29 to 0.78, SE = 0.06), low to medium genetic correlations with shoulder lesions (up to 0.4, SE = 0.1), and body condition scoring at weaning (up to 0.25, SE = 0.1). These novel phenotypes can now be recorded automatically and accurately prior to selection of the AI boars. If such recordings are included in multivariate genomic selection models, it is expected to improve the genetic progress of shoulder lesions and body condition score by weaning.

Genetic parameters of in vivo primal cuts and body composition (PigAtlas) in pigs measured by computed tomography (CT).

Genetic parameters of in vivo primal cuts in breeding pigs using computed tomography were estimated. A total of 2,439 Duroc and 1998 Landrace boars from the Topigs Norsvin boar testing station in Norway were CT scanned as part of the genetic program. In vivo primal cuts were derived from the CT images using atlas segmentation; the method called the Pig Atlas. The (co)variance estimates were obtained from univariate (heritabilities) and multivariate (correlations) animal genetic models using DMU software. The heritabilities for all primal cuts proportions (%) were intermediate to large for both breeds, h2 ranging from 0.15 to 0.50. Negative genetic correlations were found between most of the other primal cuts, and the strongest correlation was between belly and ham. Carcass lean meat percentage showed a positive correlation to shoulder and ham, but was negatively correlated to belly. In this study, in vivo primal cuts from atlas segmentation are used for genetic parameter calculations for the first time. Computed Tomography (CT) makes it possible to measure in vivo body or carcass composition. This will aid the selection response by measuring on the candidates themselves instead of using relatives. Primal cut proportion and composition measured in vivo by computed tomography and atlas segmentation show heritable variation comparable to previous post mortem studies.

Non-invasive methods for the determination of body and carcass composition in livestock: dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging and ultrasound: invited review.

The ability to accurately measure body or carcass composition is important for performance testing, grading and finally selection or payment of meat-producing animals. Advances especially in non-invasive techniques are mainly based on the development of electronic and computer-driven methods in order to provide objective phenotypic data. The preference for a specific technique depends on the target animal species or carcass, combined with technical and practical aspects such as accuracy, reliability, cost, portability, speed, ease of use, safety and for in vivo measurements the need for fixation or sedation. The techniques rely on specific device-driven signals, which interact with tissues in the body or carcass at the atomic or molecular level, resulting in secondary or attenuated signals detected by the instruments and analyzed quantitatively. The electromagnetic signal produced by the instrument may originate from mechanical energy such as sound waves (ultrasound - US), 'photon' radiation (X-ray-computed tomography - CT, dual-energy X-ray absorptiometry - DXA) or radio frequency waves (magnetic resonance imaging - MRI). The signals detected by the corresponding instruments are processed to measure, for example, tissue depths, areas, volumes or distributions of fat, muscle (water, protein) and partly bone or bone mineral. Among the above techniques, CT is the most accurate one followed by MRI and DXA, whereas US can be used for all sizes of farm animal species even under field conditions. CT, MRI and US can provide volume data, whereas only DXA delivers immediate whole-body composition results without (2D) image manipulation. A combination of simple US and more expensive CT, MRI or DXA might be applied for farm animal selection programs in a stepwise approach.

Osteochondrosis in pigs diagnosed with computed tomography: heritabilities and genetic correlations to weight gain in specific age intervals.

The aim of this study was to develop a method for scoring osteochondrosis (OC) by using information from computed tomography (CT), as well as to estimate the heritability for OC scored by means of CT (OCwCT) of the medial and lateral condyles at the distal end of the humerus or the femur of the right and left leg and the sum of these scores (OCT). In addition, we were aiming at revealing the genetic relationship between OCwCT traits and growth in different periods (days from birth to 30 kg (D30), days from 30 to 50 kg (D30_50), days from 50 to 70 kg (D50_70), days from 70 to 90 kg (D70_90), days from 90 to 100 kg (D90_100) and days from birth to 100 kg (D100)). The OCwCT was assessed for 1449 boars, and growth data were collected for these 1449 boars and additional 3779 boars tested in the same time period. All boars were tested as part of the Norsvin Landrace boar test and in the same test station. Heritabilities for OCwCT on anatomical locations varied from 0.21 (s.e. = 0.08) on the medial condyle of the right humerus to 0.06 (s.e. = 0.06) on the lateral condyle of the left femur, whereas OCT exhibited the highest heritability (h² = 0.31, s.e. = 0.09). Genetic correlations between OCT and OCwCT for the anatomical locations ranged from 0.94 (s.e. = 0.07) for OCT and OCwCT score for the medial condyle of the humerus right side to 0.26 (s.e. = 0.39) for OCT and the lateral condyle of the femur left side. Genetic correlations between D30 and OCT were medium high and unfavourable (r(g) = -0.74). As the boar gain weight, the relationship between growth rate--expressed as number of days spent growing from one interval to the next--and OCT decreased to 0.12 (s.e. = 0.19, i.e. not significantly different from zero) for the trait D90_100 kg. These changes of genetic correlation coefficients coincide with the maturing of the joint cartilage and skeletal structures. In this study, we demonstrate that CT could be used for selection against OC in breeding programmes in pigs and that the genetic correlations between growth periods and OC are decreasing over time.

Genetic parameters between slaughter pig efficiency and growth rate of different body tissues estimated by computed tomography in live boars of Landrace and Duroc.

In this study, computed tomography (CT) technology was used to measure body composition on live pigs for breeding purposes. Norwegian Landrace (L; n = 3835) and Duroc (D; n = 3139) boars, selection candidates to be elite boars in a breeding programme, were CT-scanned between August 2008 and August 2010 as part of an ongoing testing programme at Norsvin's boar test station. Genetic parameters in the growth rate of muscle (MG), carcass fat (FG), bone (BG) and non-carcass tissue (NCG), from birth to ∼100 kg live weight, were calculated from CT data. Genetic correlations between growth of different body tissues scanned using CT, lean meat percentage (LMP) calculated from CT and more traditional production traits such as the average daily gain (ADG) from birth to 25 kg (ADG1), the ADG from 25 kg to 100 kg (ADG2) and the feed conversion ratio (FCR) from 25 kg to 100 kg were also estimated from data on the same boars. Genetic parameters were estimated based on multi-trait animal models using the average information-restricted maximum likelihood (AI-REML) methodology. The heritability estimates (s.e. = 0.04 to 0.05) for the various traits for Landrace and Duroc were as follows: MG (0.19 and 0.43), FG (0.53 and 0.59), BG (0.37 and 0.58), NCG (0.38 and 0.50), LMP (0.50 and 0.57), ADG1 (0.25 and 0.48), ADG2 (0.41 and 0.42) and FCR (0.29 and 0.42). Genetic correlations for MG with LMP were 0.55 and 0.68, and genetic correlations between MG and ADG2 were -0.06 and 0.07 for Landrace and Duroc, respectively. LMP and ADG2 were clearly unfavourably genetically correlated (L: -0.75 and D: -0.54). These results showed the difficulty in jointly improving LMP and ADG2. ADG2 was unfavourably correlated with FG (L: 0.84 and D: 0.72), thus indicating to a large extent that selection for increased growth implies selection for fatness under an ad libitum feeding regime. Selection for MG is not expected to increase ADG2, but will yield faster growth of the desired tissues and a better carcass quality. Hence, we consider MG to be a better biological trait in selection for improved productivity and carcass quality. CT is a powerful instrument in conjunction with breeding, as it combines the high accuracy of CT data with measurements taken from the selection candidates. CT also allows the selection of new traits such as real body composition, and in particular, the actual MG on living animals.

Genetic parameters of fat quality in pigs measured by near-infrared spectroscopy.

Subcutaneous fat from Norwegian Landrace (n=3230) and Duroc (n=1769) pigs was sampled to investigate the sources of variation and genetic parameters of various fatty acids, fat moisture percentage and fat colour, with the lean meat percentage (LMP) also included as a trait representing the leanness of the pig. The pigs were from half-sib groups of station-tested boars included in the Norwegian pig breeding scheme. They were fed ad libitum to obtain an average of 113 kg live weight. Near-infrared spectroscopy (NIRS) was applied for prediction of the fatty acids and fat moisture percentage, and Minolta was used for the fat colour measurements. Heritabilities and genetic correlations were estimated with a multi-trait animal model using average information-restricted maximum likelihood (AI-REML) methodology. Fat from Landrace pigs had considerably more monounsaturated fatty acids, polyunsaturated fatty acids (PUFAs) and fat moisture, as well as less saturated fatty acids (SFAs) than fat from Duroc pigs. The heritability estimates (s.e. 0.03 to 0.08) for the various fatty acids were as follows: Palmitic, C16:0 (0.39 and 0.51 for Landrace and Duroc pigs, respectively); Palmitoleic, C16:1n-7 (0.41 and 0.50); Steric, C18:0 (0.46 and 0.54); Oleic, C18:1n-9 (0.67 and 0.57); Linoleic, C18:2n-6 (0.44 and 0.46); α-linolenic, C18:3n-3 (0.37 and 0.25) and n-6/n-3 ratio (0.06 and 0.01). The other fat quality traits revealed the following heritabilities: fat moisture (0.28 and 0.33), colour values in subcutaneous fat: L* (whiteness; 0.22 and 0.21), a* (redness; 0.13 and 0.24) and b* (yellowness; 0.07 and 0.17) and LMP (0.46 and 0.47). LMP showed high positive genetic correlations to PUFA (C18:2n-6 and C18:3n-3), which implies that selecting leaner pigs changes the fatty acid composition and deteriorates the quality of fat. Higher concentrations of PUFA are not beneficial as the ratio of n-6 and n-3 fatty acids becomes unfavourably high. Owing to the high genetic correlation between C18:2n-6 and C18:3n-3 and a low heritability for this ratio, the latter is difficult to change through selection. However, a small reduction in the ratio should be expected if selection aims at reducing the level of C18:2n-6. Selection for more C18:1n-9 is possible in view of the genetic parameters, which are favourable for eating quality, technological quality and human nutrition. The NIRS technology and the high heritabilities found in this study make it possible to implement fat quality traits to achieve the breeding goal in the selection of a lean pig with better fat quality.

Prediction of fat quality in pig carcasses by near-infrared spectroscopy.

This study was conducted to evaluate the potential of near-infrared (NIR) spectroscopy (NIRS) technology for prediction of the chemical composition (moisture content and fatty acid composition) of fat from fast-growing, lean slaughter pig samples coming from breeding programmes. NIRS method I: a total of 77 samples of intact subcutaneous fat from pigs were analysed with the FOSS FoodScan NIR spectrophotometer (850 to 1050 nm) and then used to predict the moisture content by using partial least squares (PLS) regression methods. The best equation obtained has a coefficient of determination for cross-validation (CV; R(2)(cv)) and a root mean square error of a CV (RMSECV) of 0.88 and 1.18%, respectively. The equation was further validated with (n = 15) providing values of 0.83 and 0.42% for the coefficient of determination for validation (R(2)(val)) and root mean square error of prediction (RMSEP), respectively. NIRS method II: in this case, samples of melted subcutaneous fat were analysed in an FOSS XDS NIR rapid content analyser (400 to 2500 nm). Equations based on modified PLS regression methods showed that NIRS technology could predict the fatty acid groups, the main fatty acids and the iodine value accurately with R(2)(cv), RMSECV, R(2)(val) and RMSEP of 0.98, 0.38%, 0.95 and 0.49%, respectively (saturated fatty acids), 0.94, 0.45%, 0.97 and 0.65%, respectively (monounsaturated fatty acids), 0.97, 0.28%, 0.99 and 0.34%, respectively (polyunsaturated fatty acids), 0.76, 0.61%, 0.84 and 0.87%, respectively (palmitic acid, C16:0), 0.75, 0.16%, 0.89 and 0.10%, respectively (palmitoleic acid, C16:1n-7), 0.93, 0.41%, 0.96 and 0.64%, respectively (steric acid, C18:0), 0.90, 0.51%, 0.94 and 0.44%, respectively (oleic acid, C18:1n-9), 0.97, 0.25%, 0.98 and 0.29% (linoleic acid, C18:2n-6), 0.68, 0.09%, 0.57 and 0.16% (α-linolenic acid, C18:3n-3) and 0.97, 0.57, 0.97 and 1.22, respectively (iodine value, calculated). The magnitude of this error showed quite good accuracy using these rapid methods in prediction of the moisture and fatty acid composition of fat from pigs involved in breeding schemes.

Prediction of fat, muscle and value in Norwegian lamb carcasses using EUROP classification, carcass shape and length measurements, visible light reflectance and computer tomography (CT).

One hundred and twenty carcasses were sampled to compare different techniques or methods for prediction of lamb carcass composition and value. Four methods that are used at the Norwegian Meat Research Centre, Animalia, were selected. These were the basic EUROP classification, the advanced EUROP classification using carcass shape and length measurements, visible light reflectance probing (GP) and Computer Tomography (CT). The different technologies were tested using an iterative approach, selecting calibration and validation data sets from the 120 carcasses randomly, where 90 carcasses were used for calibration, and 30 for validation. The best prediction models were obtained using CT, with respect to prediction error and correlation between predicted and measured value of carcass fat and muscle (in kg), and value (in NOK). Due to high cost and low operating speeds of CT, optical probing (GP) may be the second best solution of the technologies used in this study, combined with a CT dissection reference as an alternative to manual dissection.

Determination of sodium chloride in pork meat by computed tomography at different voltages.

Ground pork samples simulating the widely different chemical composition of hams during dry-cured ham production were produced and scanned by x-ray computed tomography (CT). Chemical composition accounted for most of the variation in CT values (97%). Tube voltage (80, 110, and 130 kV) affected CT value and the effect varied between different types of tissue. Sodium chloride (NaCl) was predicted in the ground samples with average prediction errors (RMSEP) as low as 0.2% to 1.0% NaCl. NaCl was also predicted in small samples of raw to dry-cured ham. When dry and fat ham samples were left out of the models, NaCl was predicted with a high precision (RMSEP 0.2% to 0.4% NaCl, R(2) > 0.99). CT can be used as a valuable, nondestructive tool to analyze distribution of and quantify NaCl in ham during dry-curing.