Dual energy X-ray absorptiometry precisely and accurately predicts lamb carcass composition at abattoir chain speed across a range of phenotypic and genotypic variables

Validation of a handheld near-infrared spectrophotometer for measurement of chemical intramuscular fat in Australian lamb

The objective of the study was to independently validate a calibrated commercial handheld near infrared (NIR) spectroscopic device and test its repeatability over time using phenotypically diverse populations of Australian lamb. Validation testing in eight separate data sub-groups (n = 1591 carcasses overall) demonstrated that the NIR device had moderate precision (R2 = 0.4-0.64, RMSEP = 0.70-1.22%) but fluctuated in accuracy between experimental site demonstrated by variable slopes (0.50-0.94) and biases (-0.86-0.02). The repeatability experiment (n = 10 carcasses) showed that time to scan post quartering affected NIR measurement from 0 to 24 h (P < 0.001). On average, NIR IMF% was 0.97% lower (P < 0.001) at 24 h (4.01% ± 0.166), compared to 0 h. There was no difference (P > 0.05) between Time 0 and 1 h or Time 0 and 4 h or between replicate scans within each time point. This study demonstrated the SOMA NIR device could predict lamb chemical IMF% with moderate precision and accuracy, however additional work is required to understand how loin preparation, blooming and surface hydration affect NIR measurement.

Dynamic correction of dual-energy x-ray absorptiometry images improves chain speed prediction of lamb composition in abattoirs

A prototype, on-line Dual Energy X-ray Absorptiometer (DXA) has shown high precision of the prediction of carcass composition for the purpose of improved sheep meat grading in the Australian lamb supply chain, albeit with small inaccuracies over time. These inaccuracies were present across hours, and more significantly across days, which were unacceptable for any accreditation of this device as an objective carcass measurement tool in Australia. This inaccuracy demanded the creation of a novel image-processing algorithm for the prototype DXA. This DXA was tested for repeatability of predictions of lamb carcass composition over minutes, hours, and days, using two developed image processing algorithms. There was high immediate repeatability for both algorithms when predicting lean muscle % in 40 lamb carcasses, with a maximum CV of 0.65% over five repeated scans. There was a decrease in the CV of the prediction of lean muscle % of 30 lambs scanned three times over a 48-h period from 5.93 to 1.19% when the superior algorithm was used. The inaccuracies of lean muscle % predictions were associated with increases in the unattenuated space pixel values in DXA images. Improvements of the current algorithm are required to demonstrate repeatability over time for the purpose of accreditation within the Australian sheep meat industry, and for possible expansion of this technology into international supply chains.

Predicting lamb carcase composition from tissue depth measured at a single point with an ultrawide-band microwave scanner

This study evaluated the ability of portable ultra-wide band microwave system (MiS) to predict lamb carcase computed tomography (CT) determined composition % of fat, lean muscle and bone. Lamb carcases (n = 343) from 6 slaughter groups were MiS scanned at the C-site (45 mm from spine midline at the 12th /13th rib) prior to CT scanning to determine the proportion of fat, muscle and bone. A machine learning ensemble stacking technique was used to construct the MiS prediction equations. Predictions were pooled and divided in 5 groups stratified for each CT composition trait (fat, lean or bone%) and a k-fold cross validation (k = 5) technique was used to test the predictions. MiS predicted CT fat% with an average RMSEP of 2.385, R2 0.78, bias 0.156 and slope 0.095. The prediction of CT lean% had an average RMSEP of 2.146, R2 0.64, bias 0.172 and slope 0.117. CT bone% prediction had an average RMSEP of 0.990, R2 0.75, bias 0.051 and slope 0.090. Predictions for CT bone% met AUS-MEAT device accreditation error tolerances on the whole range of the dataset. Predictions for CT lean% and fat% met AUS-MEAT error tolerances on a constrained dataset.

Effects of Dam and Sire Breeds on Lamb Carcass Quality and Composition in Pasture-Based Systems

This study explored the impacts of sire and dam breed on carcass quality and composition in a pasture-based system and the use of DXA to rapidly rank carcasses for leanness. Southdown (SD) and Suffolk (SF) ewes were mated to Texel (TX) or SD rams to produce seventy-nine lambs. Lambs were raised on pasture-based systems with limited grain supplementation. Lamb birth weight was greater (p < 0.01) for TX, regardless of dam breed. Lambing rate was lower (p < 0.01) for SD than SF ewes. Circulating myostatin concentrations were greater (p < 0.05) on d 42 than d 75 or d 110 but did not differ by sire breed. Texel-sired lambs had greater (p < 0.01) carcass weight, ribeye area and quality grade compared to SD-sired. Total and primal fat mass as predicted from DXA was higher (p < 0.05) in carcasses from SD than TX sires. Muscles from TX lambs had greater (p < 0.05) polyunsaturated fatty acid (PUFA) composition than SD-sired. Shear force values were influenced (p < 0.01) by dam breed, muscle cut and postmortem age but not by sire breed. The use of TX sires in pasture-based systems improved carcass leanness and muscle PUFA concentrations without altering tenderness.

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.

Economic Analysis of an Image-Based Beef Carcass Yield Estimation System in Korea

Simple Summary

Carcass grading is a vital process in the slaughterhouse and is used for the quantification of the overall value of carcasses. Since carcass grading is often performed manually by a team of grading experts, it is subject to human limitations which result in inconsistency and limited operation speed. Considering this, an automatic beef carcass yield estimation system capable of predicting 23 key yield parameters was developed. However, just like any freshly introduced system, analysis of the economic impact of the grading system is vital before deployment in any slaughterhouse business. In this study, a thorough economic analysis to justify deploying the developed beef carcass grading system in a standard slaughterhouse in South Korea was performed through a cost-benefit analysis. The analysis found that the benefits derived from using the developed system outweigh the costs of purchasing and operating the system making the endeavor economically viable.

Abstract

To minimize production costs, reduce mistakes, and improve consistency, modern-day slaughterhouses have turned to automated technologies for operations such as cutting, deboning, etc. One of the most vital operations in the slaughterhouse is carcass grading, usually performed manually by grading staff, which creates a bottleneck in terms of production speed and consistency. To speed up the carcass grading process, we developed an online system that uses image analysis and statistical tools to estimate up to 23 key yield parameters. A thorough economic analysis is required to aid slaughterhouses in making informed decisions about the risks and benefits of investing in the system. We therefore conducted an economic analysis of the system using a cost-benefit analysis (the methods considered were net present value (NPV), internal rate of return (IRR), and benefit/cost ratio (BCR)) and sensitivity analysis. The benefits considered for analysis include labor cost reduction and gross margin improvement arising from optimizing breeding practices with the use of the data obtained from the system. The cost-benefit analysis of the system resulted in an NPV of approximately 310.9 million Korean Won (KRW), a BCR of 1.72, and an IRR of 22.28%, which means the benefits outweigh the costs in the long term.

Review: Improving the nutritional, sensory and market value of meat products from sheep and cattle

This paper focuses on improving the sensory, health attributes and meat yield of beef and lamb meats. Value for meat is defined as the weight of meat × price/kg received with price linked to eating quality. To maximise value across the supply chain, accurate carcass grading systems for eating quality and yield are paramount. Grading data can then be used to target consumers' needs at given price points and then to tailor appropriate production and genetic directions. Both the grading methodologies and key phenotypes are complex and still under intensive research with international collaboration to maximise opportunities. In addition, there is value in promoting the health aspects of red meats served as whole trimmed meats. Typically, the total fat content is relatively low (less than 5%) and for forage systems, they deliver a very significant content of long-chain n-3 fatty acids. Further research is needed to clarify the healthiness or otherwise of ground beef served as burgers given the fat content is typically 20% or more. It is important to continue to improve the feedback to producers regarding the quantity and quality of the products they produce to target new value opportunities in a transparent and quantitative manner.

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

Consumer demand for high-quality healthy food is increasing, &nbsp;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.

Evaluation of an Image Analysis Approach to Predicting Primal Cuts and Lean in Light Lamb Carcasses

Simple Summary

The traditional way of estimating the carcass composition by complete dissection of muscle, fat and bone is an expensive, time-consuming and inconsistent process. The purpose of this study was to evaluate the accuracy of a simple video image analysis (VIA) system to predict the composition and primal cuts using light lamb carcasses. The six cuts of the carcasses were grouped according to their commercial value: high-value cuts (HVC), medium-value (MVC), low-value (LVC) and all of the cuts (AllC). Results showed the ability of the VIA system to estimate the weight and yield of the groups of carcass joints.

Abstract

Carcass dissection is a more accurate method for determining the composition of a carcass; however, it is expensive and time-consuming. Techniques like VIA are of great interest once they are objective and able to determine carcass contents accurately. This study aims to evaluate the accuracy of a flexible VIA system to determine the weight and yield of the commercial value of carcass cuts of light lamb. Photos from 55 lamb carcasses are taken and a total of 21 VIA measurements are assessed. The half-carcasses are divided into six primal cuts, grouped according to their commercial value: high-value (HVC), medium-value (MVC), low-value (LVC) and all of the cuts (AllC). K-folds cross-validation stepwise regression analyses are used to estimate the weights of the cuts in the groups and their lean meat yields. The models used to estimate the weight of AllC, HVC, MVC and LVC show similar results and a k-fold coefficient of determination (k-fold-R²) of 0.99 is achieved for the HVC and AllC predictions. The precision of the weight and yield of the three prediction models varies from low to moderate, with k-fold-R² results between 0.186 and 0.530, p < 0.001. The prediction models used to estimate the total lean meat weight are similar and low, with k-fold-R² results between 0.080 and 0.461, p < 0.001. The results confirm the ability of the VIA system to estimate the weights of parts and their yields. However, more research is needed on estimating lean meat yield.

Non-Destructive Imaging and Spectroscopic Techniques for Assessment of Carcass and Meat Quality in Sheep and Goats: A Review

In the last decade, there has been a significant development in rapid, non-destructive and non-invasive techniques to evaluate carcass composition and meat quality of meat species. This article aims to review the recent technological advances of non-destructive and non-invasive techniques to provide objective data to evaluate carcass composition and quality traits of sheep and goat meat. We highlight imaging and spectroscopy techniques and practical aspects, such as accuracy, reliability, cost, portability, speed and ease of use. For the imaging techniques, recent improvements in the use of dual-energy X-ray absorptiometry, computed tomography and magnetic resonance imaging to assess sheep and goat carcass and meat quality will be addressed. Optical technologies are gaining importance for monitoring and evaluating the quality and safety of carcasses and meat and, among them, those that deserve more attention are visible and infrared reflectance spectroscopy, hyperspectral imagery and Raman spectroscopy. In this work, advances in research involving these techniques in their application to sheep and goats are presented and discussed. In recent years, there has been substantial investment and research in fast, non-destructive and easy-to-use technology to raise the standards of quality and food safety in all stages of sheep and goat meat production.