Objective carcass measurement technologies: Latest developments and future trends

The association of P2 and lean % estimates from commercial measurement systems with computed tomography determined composition within Australian pork abattoirs

Pork carcasses were obtained from three abattoirs in Australia (n = 345) where technologies enabled collection of post slaughter measures of P2 fat depth (mm) (Hennessy Grading Probe (HGP), AutoFom III, PorkScan Lite) and estimates of carcass lean % (HGP, AutoFom III, PorkScan Plus). Computed tomography (CT) was used to scan carcasses and determine lean and fat %, with the strength of associations with abattoir measurement devices determined. The AutoFom III lean % demonstrated the strongest associations with whole carcass CT lean % (R2 0.63, RMSE 1.73) and fat % (R2 0.68, RMSE 1.80) and with section (fore, loin, belly and hind) CT composition. The association of P2 from AutoFom III was lower in comparison, however remained superior to other commercial devices (PorkScan Lite and HGP). Porkscan Plus lean % demonstrated moderate associations with whole carcass and section CT lean and fat %, with R2 values generally less than half those of the AutoFom III. The HGP demonstrated weakest associations with CT lean and fat % using either lean % or P2 outputs, which is likely related to data being collected from only the P2 measurement site. This is the first experiment to compare the strength of associations between multiple pork abattoir measurement devices and CT lean and fat % in Australia. P2 is the current industry standard for the assessment of lean yield in pork, however demonstrates weaker associations with carcass CT composition than devices capable of capturing multiple measures across the carcass like AutoFom III and PorkScan Plus.

Ultra-wideband microwave precisely and accurately predicts sheepmeat hot carcase GR tissue depth

A portable ultra-wideband microwave system (MiS) coupled with an antipodal slot Vivaldi patch antenna (VPA) was used as an objective measurement technology to predict sheep meat carcase GR tissue depth, tested against AUS-MEAT national accreditation standards. Experiment one developed the MiS GR tissue depth prediction equation using lamb carcasses (n = 832) from two slaughter groups. To create the prediction equations, a two layered machine learning stacking ensemble technique was used. The performance of this equation was tested within the dataset using a k-fold cross validation (k = 5), which demonstrated excellent precision and accuracy with an average R2 of 0.91, RMSEP 2.11, bias 0.39 and slope 0.03. Experiment two tested the prediction equation against the AUS-MEAT GR tissue depth accreditation framework which stipulates predictions from a device must assign the correct fat score, with a tolerance of ±2 mm of the score boundary, and 90% accuracy. For a device to be accredited three measurements captured within the same device, as well as measurements across three different devices, must meet the AUS-MEAT error thresholds. Three MiS devices scanned lamb carcases (n = 312) across three slaughter days. All three MiS devices met the AUS-MEAT accreditation thresholds, accurately predicting GR tissue depth 96.1-98.4% of the time. Between the different devices, the measurement accuracy was 99.4-100%, and within the same device, the measurement accuracy was 99.7-100%. Based on these results MiS achieved AUS-MEAT device accreditation as an objective technology to predict GR tissue depth.

Deep Learning-Based Automated Approach for Determination of Pig Carcass Traits

Pig carcass traits are among the most economically significant characteristics and are crucial for genetic selection in breeding and enhancing the economic efficiency. Standardized and automated carcass phenotyping can greatly enhance the measurement efficiency and accuracy, thereby facilitating the selection and breeding of superior pig carcasses. In this study, we utilized phenotypic images and data from 3912 pigs to propose a deep learning-based approach for the automated determination of pig carcass phenotypic traits. Using the YOLOv8 algorithm, our carcass length determination model achieves an average accuracy of 99% on the test set. Additionally, our backfat segmentation model, YOLOV8n-seg, demonstrates robust segmentation performance, with a Mean IoU of 89.10. An analysis of the data distribution comparing manual and model-derived measurements revealed that differences in the carcass straight length are primarily concentrated between -2 cm and 4 cm, while differences in the carcass diagonal length are concentrated between -3 cm and 2 cm. To validate the method, we compared model measurements with manually obtained data, achieving coefficients of determination (R2) of 0.9164 for the carcass straight length, 0.9325 for the carcass diagonal length, and 0.7137 for the backfat thickness, indicating high reliability. Our findings provide valuable insights into automating carcass phenotype determination and grading in pig production.

Using Image Analysis Technique for Predicting Light Lamb Carcass Composition

Over the years, numerous techniques have been explored to assess the composition and quality of sheep carcasses. This study focuses on the utilization of video image analysis (VIA) to evaluate the composition of light lamb carcasses (4.52 ± 1.34 kg, mean cold carcass weight ± SD). Photographic images capturing the lateral and dorsal sides of fifty-five light lamb carcasses were subjected to analysis. A comprehensive set of measurements was recorded, encompassing dimensions such as lengths, widths, angles, areas, and perimeters, totaling 21 measurements for the lateral view images and 29 for the dorsal view images. K-Folds stepwise multiple regression analyses were employed to construct prediction models for carcass tissue weights (including muscle, subcutaneous fat, intermuscular fat, and bone) and their respective percentages. The most effective prediction equations were established using data from cold carcass weight (CCW) and measurements from both dorsal and lateral views. These models accounted for a substantial portion of the observed variation in the weights of all carcass tissues (with K-fold-R2 ranging from 0.83 to 0.98). In terms of carcass tissue percentages, although the degree of variation explained was slightly lower (with K-fold-R2 ranging from 0.41 to 0.78), the VIA measurements remained integral to the predictive models. These findings underscore the efficacy of VIA as an objective tool for assessing the composition of light lamb carcasses, which are carcasses weighing ≈ 4-8 kg.

Prediction of carcass traits in fattening Chios and Serres lambs using real-time ultrasonography and live body weight measurements pre-slaughter

The objective of this study was to assess the capability of predicting carcass traits and meat cuts weights, in fattening lambs of indigenous Greek dairy sheep breeds, using ultrasound measurements and live body weight measurements pre-slaughter. A total of 187 lambs of Chios and Serres breeds were involved in the study. Body condition score, live body weight (LBW), and ultrasound measurements of Longissimus lumborum muscle depth (LMD) and subcutaneous fat thickness (SFT) at the lumbar region were recorded pre-slaughter. After slaughter, the carcasses were classified using five-degree grading systems for muscle development and fat deposition, while hot (HCW) and cold carcass (CCW) and meat cuts weights were measured. The statistical analyses included descriptive statistics and linear regression models to estimate the fixed effects of sex and the covariances of LBW, BCS, and ultrasound measurements on the studied traits. High R2 values (0.60 ≤ R2 ≤ 0.92) were observed in the models predicting HCW, CCW, forequarter, leg chump on shank off, the short loin, the eye of the short loin, and foreshank weights. Among the models estimated LMD, SFT, and LBW as significant predictors, the ones predicting hot and cold carcass weights, the short loin, the eye of the short loin, and the eye of the rack weights were successfully validated. Other models including BCS, LBW, sex, and either one or none of the ultrasonography measurements as predictors were also validated and presented.

Challenges and future perspectives for the European grading of pig carcasses - A quality view

This study sought to evaluate pig carcass grading, describing the existing approaches and definitions, and highlighting the vision for overall quality grading. In particular, the current state of pig carcass grading in the European Union (SEUROP system), its weaknesses, and the challenges to achieve more uniformity and harmonization across member states were described, and a broader understanding of pig carcass value, which includes a vision for the inclusion of meat quality aspects in the grading, was discussed. Finally, the noninvasive methods for the on-line evaluation of pig carcass and meat quality (hereafter referred to as pork quality), and the conditions for their application were discussed. As the way pigs are raised (especially in terms of animal welfare and environmental impact), and more importantly, their perception of pork quality, is becoming increasingly important to consumers, the ideal grading of pigs should comprise pork quality aspects. As a result, a forward-looking "overall quality" approach to pork grading was proposed herein, in which grading systems would be based on the shared vision for pork quality (carcass and meat quality) among stakeholders in the pig industry and driven by consumer expectations with respect to the product. Emerging new technologies provide the technical foundation for such perspective; however, integrating all knowledge and technologies for their practical application to an "overall quality" grading approach is a major challenge. Nonetheless, such approach aligns with the recent vision of Industry 5.0, i.e. a model for the next level of industrialization that is human-centric, resilient, and sustainable.

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.

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.

Post-mortem muscle proteome of crossbred bulls and steers: Relationships with carcass and meat quality

This study investigated the skeletal muscle proteome of crossbred bulls and steers with the aim of explaining the differences in carcass and meat quality traits. Therefore, 640 post-weaning Angus-Nellore calves were fed a high-energy diet for a period of 180 days. In the feedlot trial, comparisons of steers (n = 320) and bulls (n = 320) showed lower (P < 0.01) average daily gain (1.38 vs. 1.60 ± 0.05 kg/d), final body weight (547.4 vs. 585.1 ± 9.3 kg), which resulted in lower hot carcass weight (298.4 vs. 333.7 ± 7.7 kg) and ribeye area (68.6 vs. 81.0 ± 2.56 cm²). Steers had higher (P < 0.01) carcass fatness, meat color parameters (L*, a*, b*, chroma (C*), hue (h°)) and lower ultimate pH. Moreover, lower (P < 0.01) Warner-Bratzler shear force (WBSF) were observed in steers compared to bulls (WBSF = 3.68 vs. 4.97 ± 0.08 kg; and 3.19 vs. 4.08 ± 0.08 kg). The proteomic approach using two-dimensional electrophoresis, mass spectrometry and bioinformatics procedures revealed several differentially expressed proteins between steers and bulls (P < 0.05). Interconnected pathways and substantial changes were revealed in biological processes, molecular functions, and cellular components between the post-mortem muscle proteomes of the compared animals. Steers had increased (P < 0.05) abundance of proteins related to energy metabolism (CKM, ALDOA, and GAPDH), and bulls had greater abundance of proteins associated with catabolic (glycolysis) processes (PGM1); oxidative stress (HSP60, HSPA8 and GSTP1); and muscle structure and contraction (TNNI2 and TNNT3). The better carcass (fatness and marbling degree) and meat quality traits (tenderness and color parameters) of steers were associated with higher abundance of key proteins of energy metabolism and lower abundance of enzymes related to catabolic processes, oxidative stress, and proteins of muscle contraction SIGNIFICANCE: Sexual condition of cattle is known to be an important factor affecting animal performances and growth as well as the carcass and meat quality traits. The investigation of skeletal muscle proteome help a better understanding of the origin of the differences in quality traits between bulls and steers. The inferior meat quality of bulls was found to be due to the greater expression of proteins associated with primary and catabolic processes, oxidative stress, and muscle contraction. Steers had greater expression of proteins, from which several are known biomarkers of beef quality (mainly tenderness).

Carcass, meat quality traits, and economic analysis of Nellore bulls fed with finishing feedlot diets containing mechanically processed corn silage

Effects of mechanical processing (MP) of corn silage and its inclusion in feedlot diets on carcass and meat quality traits of Nellore (Bos indicus) were analyzed. Seventy-two bulls aged approximately 18 months and with an initial average body weight of 392.8 ± 22.3 kg were used. The experimental design was a 2 × 2 factorial arrangement, considering the concentrate-roughage (C:R) ratio (40:60 or 20:80), MP of silage and their interactions. After slaughter, hot carcass weight (HCW), pH, temperature, backfat thickness (BFT), and ribeye area (REA), yields of meat cuts (tenderloin, striploin, ribeye steak, neck steak, and sirloin cap), meat quality traits and economic analysis were evaluated. A lower final pH was found in the carcasses of animals consuming diets containing MP versus unprocessed silage (pH = 5.81 versus 5.93). Carcass variables (HCW, BFT, and REA) and meat cut yields were not affected by treatments. The C:R 20:80 increased the intramuscular fat (IMF) content by approximately 1%, without affecting moisture, ash, and protein contents. Meat/fat color (L*, a* and b*) and Warner-Bratzler shear force (WBSF) were similar among treatments. The results indicated that the MP of corn silage in finishing diets can provide better carcass pH results in Nellore bulls, without negatively influencing carcass weight, fatness, and meat tenderness (WBSF). The IMF content of meat was slightly improved using a C:R 20:80 and lower total costs per arroba produced (3.5%), daily costs per animal/day (4.2%), and cost per ton of feeds (5.15%) were found with MP silage.

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.

Meat Quality and Muscle Tissue Proteome of Crossbred Bulls Finished under Feedlot Using Wet Distiller Grains By-Product

Wet distiller grains (WDG) are a corn by-product rich in protein and fiber that can be used in feedlot diets. This study evaluated F1 Angus-Nellore bulls fed on a control diet vs. WDG (n = 25/treatment). After a period of 129 days on these feeds, the animals were slaughtered and Longissimus thoracis samples were collected for both a meat quality evaluation and gel-based proteomic analyses. A greater ribeye area (99.47 cm²) and higher carcass weight (333.6 kg) (p < 0.05) were observed in the WDG-finished cattle compared to the control (80.7 cm²; 306.3 kg). Furthermore, there were differences (p < 0.05) in the intramuscular fat between the WDG and control animals (IMF = 2.77 vs. 4.19%), which led to a significant decrease (p < 0.05) in saturated fatty acids (FA). However, no differences (p > 0.10) were observed in terms of tenderness, evaluated using Warner–Bratzler shear force (WBSF). The proteomic and bioinformatic analyses revealed substantial changes in the biological processes, molecular functions, and cellular components of the WDG-finished cattle compared to the control. Proteins related to a myriad of interconnected pathways, such as contractile and structural pathways, energy metabolism, oxidative stress and cell redox homeostasis, and transport and signaling. In this experiment, the use of WDG supplementation influenced the protein expression of several proteins, some of which are known biomarkers of beef quality (tenderness and color), as well as the protein–protein interactions that can act as the origins of increases in muscle growth and reductions in IMF deposition. However, despite the effects on the proteome, the tenderness, evaluated by WBSF, and fatty acid profile were not compromised by WDG supplementation.

Assessing the Feasibility of Using Kinect 3D Images to Predict Light Lamb Carcasses Composition from Leg Volume

This study aimed to evaluate the accuracy of the leg volume obtained by the Microsoft Kinect sensor to predict the composition of light lamb carcasses. The trial was performed on carcasses of twenty-two male lambs (17.6 ± 1.8 kg, body weight). The carcasses were split into eight cuts, divided into three groups according to their commercial value: high-value, medium value, and low-value group. Linear, area, and volume of leg measurements were obtained to predict carcass and cuts composition. The leg volume was acquired by two different methodologies: 3D image reconstruction using a Microsoft Kinect sensor and Archimedes principle. The correlation between these two leg measurements was significant (r = 0.815, p < 0.01). The models to predict cuts and carcass traits that include leg Kinect 3D sensor volume are very good in predicting the weight of the medium value and leg cuts (R² of 0.763 and 0.829, respectively). Furthermore, the model, which includes the Kinect leg volume, explained 85% of its variation for the carcass muscle. The results of this study confirm the good ability to estimate cuts and carcass traits of light lamb carcasses with leg volume obtained with the Kinect 3D sensor.

Predicting Beef Carcass Fatness Using an Image Analysis System

Simple Summary

The degree of conformation and the degree of fatness are the primary parameters taken by the European beef carcass classification system (the SEUROP system) for assessing carcass quality and pricing. Evaluations have conventionally been performed by graders suitably trained using photographic standards but in recent years new techniques have been developed to enhance grading accuracy and objectivity. This study reports a method that uses an image analysis to assess the degree of fatness of beef carcasses. The results obtained show that the accuracy significantly improves by using this image analysis method compared with the conventional method that assigns scores based on photographic standards. It would therefore be appropriate to implement this technique on slaughter lines to improve the beef carcass classification system.

Abstract

The amount and distribution of subcutaneous fat is an important factor affecting beef carcass quality. The degree of fatness is determined by visual assessments scored on a scale of five fatness levels (the SEUROP system). New technologies such as the image analysis method have been developed and applied in an effort to enhance the accuracy and objectivity of this classification system. In this study, 50 young bulls were slaughtered (570 ± 52.5 kg) and after slaughter the carcasses were weighed (360 ± 33.1 kg) and a SEUROP system fatness score assigned. A digital picture of the outer surface of the left side of the carcass was taken and the area of fat cover (fat area) was measured using an image analysis system. Commercial cutting of the carcasses was performed 24 h post-mortem. The fat trimmed away on cutting (cutting fat) was weighed. A regression analysis was carried out for the carcass cutting fat (y-axis) on the carcass fat area (x-axis) to establish the accuracy of the image analysis system. A greater accuracy was obtained by the image analysis (R² = 0.72; p < 0.001) than from the visual fatness scores (R² = 0.66; p < 0.001). These results show the image analysis to be more accurate than the visual assessment system for predicting beef carcass fatness.

Recent developments in the objective measurement of carcass and meat quality for industrial application

This paper summarises the contents of this Special Edition. The papers cover a range of advanced technologies for the objective measurement of carcass characteristics that influence the yield and potential eating quality of beef and lamb carcasses. All the research has been carried out in Australia and New Zealand and has been centrally funded with collaboration between various groups. This Special Edition is timely since the meat industry is coming under pressure on environmental grounds in addition to health warnings about excessive meat consumption. In this respect it is encouraging that so many of the papers relate to eating quality. The emphasis on objective methods is also important as moving away from traditional subjective grading will improve accuracy and consistency and thereby increase efficiency. Some differences in the approach taken in other parts of the world are discussed.

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.