Rapid and non-destructive determination of lean fat and bone content in beef using dual energy X-ray absorptiometry

Genome-wide association study for primal cut lean traits in Canadian beef cattle

This study identified genomic variants and underlying candidate genes related to the whole carcass and individual primal cut lean content in Canadian commercial crossbred beef cattle. Genotyping information of 1035 crossbred beef cattle were available alongside estimated and actual carcass lean meat yield and individual primal cut lean content in all carcasses. Significant fixed effects and covariates were identified and included in the animal model. Genome-wide association analysis were implemented using the weighted single-step genomic best linear unbiased prediction (WssGBLUP). A number of candidate genes identified linked to lean tissue production were unrelated to estimated lean meat yield and were specific to the actual lean traits. Among these, 41 genes were common for actual lean traits, on specific regions of BTA4, BTA13 and BTA25 indicating potential involvement in lean mass synthesis. Therefore, the results suggested the inclusion of primal cut lean traits as a selection objective in breeding programs with consideration of further functional studies of the identified genes could help in optimizing lean yield for maximal carcass value.

Prediction of primal and retail cut weights, tissue composition and yields of youthful cattle carcasses using computer vision systems; whole carcass camera and/or ribeye camera

The application of two computer vision systems (CVS) was evaluated to predict primal and retail cut composition in youthful beef carcasses. Left carcass sides from a total of 634 animals were broken down into primal cuts, scanned using dual-energy x-ray absorptiometry for the prediction of tissue composition and fabricated into retail cuts. Cold carcass camera (CCC) images led to higher R² values than hot carcass camera (HCC) images. The CVS coefficients of prediction for the primal cut weights ranged from 0.61 to 0.97. For the primal cut tissue composition predictions, R² values ranged from 0.09 for Brisket HCC bone prediction to 0.82 for Chuck CCC fat prediction. Retail cut weight estimations had similar R² values, ranging from 0.10 for IMPS 112 (Ribeye roll-denuded ribeye) to 0.99 for IMPS 113C (semi-boneless chuck) both using CCC. The results suggest the feasibility of CVS technologies to predict beef primal and retail cuts weights together with tissue composition, and yield percentages.

Estimation of genetic parameters for primal tissue component traits in commercial crossbred beef cattle

Knowledge of genetic parameters is required to select for optimal yield of primal cuts that may be used as the selection criteria for designing future breeding programs. This study aimed to estimate the heritability, as well as genetic and phenotypic correlations of primal cut lean and fat tissue components, and carcass traits in Canadian crossbred beef cattle. All tissue component traits presented a medium to high heritability (lean 0.41 to 0.61; fat 0.46 to 0.62; bone 0.22 to 0.48), which indicates a probable increase in their response to genetic selection. In addition, high genetic correlations were found among the primal cut lean trait group (0.63 to 0.94) and fat trait group (0.63 to 0.94), as well as strong negative correlations between lean and fat component traits (-0.63 to -1). Therefore, results suggested inclusion of primal cut tissue composition traits as a selection objective in breeding programs with consideration of correlations among the traits could help in optimizing lean yield for the highest carcass value.

Assessing dual-energy X-ray absorptiometry prediction of intramuscular fat content in beef longissimus steaks

This study assessed the capability of dual-energy X-ray absorptiometry (DEXA) to predict intramuscular fat (IMF) content of beef longissimus steaks against chemical IMF as the gold standard. DEXA performance of fat% prediction was assessed using a leave-one-out cross validation method among Angus and Nellore steaks, which generated a chemical fat% range of 14.05-36.82% and 2.46-7.84%, respectively, and using pooled data. There was a significant positive association between DEXA predicted fat and chemical fat content. However, higher precision was found for pooled data (R² = 0.95, RMSECV = 1.95) and Angus (R² = 0.75, RMSECV = 2.39) than Nellore (R² = 0.15, RMSECV = 1.22) group. Accuracy also had the same response with average slope values close to 1 for pooled data and Angus and a lower value (0.42) for Nellore group. DEXA precisely predicts IMF content across a wide range of fat content. However, its precision and accuracy of prediction within low-fat content samples are lower than in high-fat content.

Estimation of carcass chemical composition in beef-on-dairy cattle using dual-energy X-ray absorptiometry (DXA) scans of cold half-carcass or 11th rib cut

The aim of the present study was to estimate the chemical composition (water, lipid, protein, mineral, and energy contents) of carcasses measured postmortem using dual-energy X-ray absorptiometry (DXA) scans of cold half-carcass or 11th rib cut. One hundred and twenty beef-on-dairy (dam: Swiss Brown, sire: Angus, Limousin, or Simmental) bulls (n = 66), heifers (n = 42), and steers (n = 12) were included in the study. The reference carcass composition measured after grinding, homogenization, and chemical analyses was estimated from DXA variables using simple or multiple linear regressions with model training on 70% (n = 84) and validation on 30% (n = 36) of the observations. In the validation step, the estimates of water and protein masses from the half-carcass (R2 = 0.998 and 0.997; root mean square error of prediction [RMSEP], 1.0 and 0.5 kg, respectively) and 11th rib DXA scans (R2 = 0.997 and 0.996; RMSEP, 1.5 and 0.5 kg, respectively) were precise. Lipid mass was estimated precisely from the half-carcass DXA scan (R2 = 0.990; RMSEP = 1.0 kg) with a slightly lower precision from the 11th rib DXA scan (R2 = 0.968; RMSEP = 1.7 kg). Mineral mass was estimated from half-carcass (R² = 0.975 and RMSEP = 0.3 kg) and 11th rib DXA scans (R2 = 0.947 and RMSEP = 0.4 kg). For the energy content, the R2 values ranged from 0.989 (11th rib DXA scan) to 0.996 (half-carcass DXA scan), and the RMSEP ranged from 36 (half-carcass) to 55 MJ (11th rib). The proportions of water, lipids, and energy in the carcasses were also precisely estimated (R2 ≥ 0.882) using either the half-carcass (RMSEP ≤ 1.0%) or 11th rib-cut DXA scans (RMSEP ≤ 1.3%). Precision was lower for the protein and mineral proportions (R2 ≤ 0.794, RMSEP ≤ 0.5%). The cattle category (sex and breed of sire) effect was observed only in some estimative models for proportions from the 11th rib cut. In conclusion, DXA imaging of either a cold half-carcass or 11th rib cut is a precise method for estimating the chemical composition of carcasses from beef-on-dairy cattle.

The application of computer vision systems in meat science and industry - A review

Computer vision systems (CVS) are applied to macro- and microscopic digital photographs captured using digital cameras, ultrasound scanners, computer tomography, and wide-angle imaging cameras. Diverse image acquisition devices make it technically feasible to obtain information about both the external features and internal structures of targeted objects. Attributes measured in CVS can be used to evaluate meat quality. CVS are also used in research related to assessing the composition of animal carcasses, which might help determine the impact of cross-breeding or rearing systems on the quality of meat. The results obtained by the CVS technique also contribute to assessing the impact of technological treatments on the quality of raw and cooked meat. CVS have many positive attributes including objectivity, non-invasiveness, speed, and low cost of analysis and systems are under constant development an improvement. The present review covers computer vision system techniques, stages of measurements, and possibilities for using these to assess carcass and meat quality.

Estimation of Empty Body and Carcass Chemical Composition of Lactating and Growing Cattle: Comparison of Imaging, Adipose Cellularity, and Rib Dissection Methods

The aim of present study was to compare in vivo and post mortem methods for estimating the empty body (EB) and carcass chemical compositions of Simmental lactating and growing cattle. Indirect methods were calibrated against the direct post mortem reference determination of chemical compositions of EB and carcass, determined after grinding and analyzing the water, lipid, protein, mineral masses, and energy content. The indirect methods applied to 12 lactating cows and 10 of their offspring were ultrasound (US), half-carcass and 11th rib dual-energy X-ray absorptiometry (DXA) scans, subcutaneous and perirenal adipose cell size (ACS), and dissection of the 11th rib. Additionally, three-dimensional (3D) images were captured for 8 cows. Multiple linear regressions with leave-one-out-cross-validations were tested between predictive variables derived from the methods tested, and the EB and carcass chemical compositions. Partial least square regressions were used to estimate body composition with morphological traits measured on 3D images. Body weight (BW) alone estimated the EB and carcass composition masses with a root mean squared error of prediction (RMSEP) for the EB from 1 kg for minerals to 12.4 kg for lipids, and for carcass from 0.9 kg for minerals to 7.8 kg for water. Subcutaneous adipose tissue thickness measured by US was the most accurate in vivo predictor when associated with BW to estimate chemical composition, with the EB lipid mass RMSEP = 11 kg and R² = 0.75; carcass water mass RMSEP = 6 kg and R² = 0.98; and carcass energy content RMSEP = 236 MJ and R² = 0.91. Post mortem, carcass lipid mass was best estimated by half-carcass DXA scan (RMSEP = 2 kg, R² = 0.98), 11th rib DXA scan (RMSEP = 3 kg, R² = 0.96), 11th rib dissection (RMSEP = 4 kg, R² = 0.92), and perirenal ACS (RMSEP = 6 kg, R² = 0.79) in this respective order. The results obtained by 11th rib DXA scan were accurate and close to the half-carcass DXA scan with a reduction in scan time. Morphological traits from 3D images delivered promising estimations of the cow EB and carcass chemical component masses with an error less than 13 kg for the EB lipid mass and than 740 MJ for the EB energy. Future research is required to test the 3D imaging method on a larger number of animals to confirm and quantify its interest in estimating body composition in living animals.

Official Position of the Brazilian Association of Bone Assessment and Metabolism (ABRASSO) on the evaluation of body composition by densitometry-part II (clinical aspects): interpretation, reporting, and special situations

OBJECTIVE

To present an updated and evidence-based guideline for the use of dual-energy x-ray absorptiometry (DXA) to assess body composition in clinical practice.

MATERIALS AND METHODS

This Official Position was developed by the Scientific Committee of the Brazilian Association of Bone Assessment and Metabolism (Associação Brasileira de Avaliação Óssea e Osteometabolismo, ABRASSO) and experts in the field who were invited to contribute to the preparation of this document. The authors searched current databases for relevant publications in the area of body composition assessment. In this second part of the Official Position, the authors discuss the interpretation and reporting of body composition parameters assessed by DXA and the use of DXA for body composition evaluation in special situations, including evaluation of children, persons with HIV, and animals.

CONCLUSION

This document offers recommendations for the use of DXA in body composition evaluation, including indications, interpretation, and applications, to serve as a guiding tool in clinical practice and research for health care professionals in Brazil.

Prediction of carcass composition and meat and fat quality using sensing technologies: A review

Consumer demand for high-quality healthy food is increasing,  thus meat processors require the means toassess these rapidly, accurately, and inexpensively. Traditional methods forquality assessments are time-consuming, expensive, invasive, and have potentialto negatively impact the environment. Consequently, emphasis has been put onfinding non-destructive, fast, and accurate technologies for productcomposition and quality evaluation. Research in this area is advancing rapidlythrough recent developments in the areas of portability, accuracy, and machinelearning. The present review, therefore, critically evaluates and summarizes developmentsof popular non-invasive technologies (i.e., from imaging to spectroscopicsensing technologies) for estimating beef, pork, and lamb composition andquality, which will hopefully assist in the implementation of thesetechnologies for rapid evaluation/real-timegrading of livestock products in the nearfuture.

Impact of genetic potential for residual feed intake and diet fed during early- to mid-gestation in beef heifers on carcass characteristics and meat quality attributes of their castrated male offspring

Carcass attributes of steers were examined for influences of selection for residual feed intake (RFI), and exposure to different levels of prenatal nutrition. Heifers characterized for RFI corrected for backfat were mated to bulls with genetic potential for either High-RFI or Low-RFI, such that the progeny were expected to be H/H or L/L RFI (sire/dam). Pregnant heifers were assigned to a low diet (Ldiet; 0.40 kg/d ADG), or moderate diet (Mdiet; 0.57 kg/d ADG), from 30 to 150 days of gestation, after which all heifers were managed similarly. Steer offspring (n = 23) were also managed similarly until slaughter. Dressing percentage of steers from H-RFI dams/sires exposed to Ldiet during gestation was lower than all other groups (P = 0.02). Marbling was greater for steers from H-RFI parents, as was fat content of longissimus thoracis et lumborum and triceps brachii (P ≤ 0.02). Results suggest that parental selection for RFI and prenatal maternal diet can influence carcass characteristics of progeny.

Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry

X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, and fruit infestations. This article presents a processing methodology for unsupervised foreign object detection based on dual-energy X-ray absorptiometry (DEXA). A novel thickness correction model is introduced as a pre-processing technique for DEXA data. The aim of the model is to homogenize regions in the image that belong to the food product and to enhance contrast where the foreign object is present. In this way, the segmentation of the foreign object is more robust to noise and lack of contrast. The proposed methodology was applied to a dataset of 488 samples of meat products acquired from a conveyor belt. Approximately 60% of the samples contain foreign objects of different types and sizes, while the rest of the samples are void of foreign objects. The results show that samples without foreign objects are correctly identified in 97% of cases and that the overall accuracy of foreign object detection reaches 95%.

Carcass and Primal Composition Predictions Using Camera Vision Systems (CVS) and Dual-Energy X-ray Absorptiometry (DXA) Technologies on Mature Cows

This study determined the potential of computer vision systems, namely the whole-side carcass camera (HCC) compared to the rib-eye camera (CCC) and dual energy X-ray absorptiometry (DXA) technology to predict primal and carcass composition of cull cows. The predictability (R²) of the HCC was similar to the CCC for total fat, but higher for lean (24.0%) and bone (61.6%). Subcutaneous fat (SQ), body cavity fat, and retail cut yield (RCY) estimations showed a difference of 6.2% between both CVS. The total lean meat yield (LMY) estimate was 22.4% better for CCC than for HCC. The combination of HCC and CCC resulted in a similar prediction of total fat, SQ, and intermuscular fat, and improved predictions of total lean and bone compared to HCC/CCC. Furthermore, a 25.3% improvement was observed for LMY and RCY estimations. DXA predictions showed improvements in R² values of 26.0% and 25.6% compared to the HCC alone or the HCC + CCC combined, respectively. These results suggest the feasibility of using HCC for predicting primal and carcass composition. This is an important finding for slaughter systems, such as those used for mature cattle in North America that do not routinely knife rib carcasses, which prevents the use of CCC.

Development and validation of the Canadian retail cut beef yield grades

The United States Department of Agriculture (USDA) system has five retail cut yield (RCY) classes, whereas the former Canadian system had three total lean yield (TLY) classes. A total of 720 beef carcasses were used to develop a modified grade ruler, harmonizing the Canadian grades into five classes. Beef carcasses (n = 750) from three Canadian federally inspected facilities were graded using both USDA and Canadian (harmonized ruler) systems for external validation. Agreement between the USDA-RCY and the Canadian TLY was high (R2 = 0.80). The validation between the harmonized ruler and the USDA-RCY showed a standard deviation of the difference of 1.32 and a coefficient of concordance of 0.83.

Yield measurement is valuable for pricing beef carcasses

Context The most common way of pricing beef carcasses is through a price grid. Most processors make greater profit from higher-quality carcasses, which are those with higher meat yield and eating quality. Aims The aim of this study was to calculate the value of diverse carcasses and compare pricing mechanisms on their ability to discriminate variation in meat yield and predicted eating quality. Methods Hereford cross steer carcasses (153) were boned out to record saleable meat and yield. Six methods were used to calculate carcass price (AU$/kg). All were adjusted to the same average carcass value to allow comparisons, assuming that the overall payment does not change, but comparing the effect of having greater premiums and discounts. The six prices were based on a commercial grid, grid plus eating quality premium, yield of saleable meat only (constant price for all saleable meat), yield with eating quality premium, then the yield prices with optimum (quadratic) weight and fatness penalties based on grid optimums. Key results Measurements of meat quality (eye muscle area and marble score) or saleable meat yield accounted for no variation in the grid price. However, measurement of yield accounted for substantial variation in prices calculated from yield and eating quality. Conclusions The current grids do not encourage high-quality meat production and, assuming that yield and eating quality are important to processors, an actual measurement of yield is crucial to guide processing decisions (e.g. cutting plans) to maximise carcass value and feed market signals back to beef producers. Implications Improved measurement of meat yield is required if carcass prices are to reflect carcass quality or the potential value captured.

A prototype rapid dual energy X-ray absorptiometry (DEXA) system can predict the CT composition of beef carcases

The development of a novel rapid dual energy X-ray absorptiometry (DEXA) system provides the opportunity to improve measurement of beef carcase composition. A prototype rapid DEXA system was built in a shipping container to scan 51 beef carcases selected for a wide range in weight and fatness. One side of each carcase was spray chilled and the other conventionally chilled overnight before being quartered for DEXA scanning and then being cut into 16 pieces for CT scanning to determine carcase composition. Spray chilling did not impact DEXA prediction of CT composition, with the DEXA system describing 89%, 95%, and 87% of the variation in beef carcase CT lean %, fat % and bone %, with a root mean square error of prediction of 2.31 lean %, 2.15 fat %, and 1.12 bone % units. These results demonstrate that the novel rapid DEXA system has excellent capacity to predict CT composition in beef carcases.

Using dual energy X-ray absorptiometry to estimate commercial cut weights at abattoir chain-speed

This experiment assessed the ability of an on-line dual energy x-ray absorptiometer (DEXA) installed at a commercial abattoir to determine commercial cut weights in lamb carcases at abattoir chain-speed. 200 lamb carcases were scanned using a DEXA that was trained to predict the computed tomography determined proportions of fat, lean, and bone. Models were then trained using hot carcase weight and, DEXA fat% value or GR tissue depth to predict cut weight. Results from validation tests of DEXA models demonstrated excellent precision for predicting cut weight, in most cases describing more than 85% of the variation, and RMSE values that represented between 5 and 13% of the average weight of each cut. For most cuts these weight predictions were superior to those informed by GR tissue depth. This precision was maintained upon validation. Additional analyses utilised pixel information from the fore, saddle, and hind sections of DEXA images. This further enhanced the predictive power of cut weight models.