Assessing beef carcass tissue weights using computed tomography spirals of primal cuts

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.

A Review on the Commonly Used Methods for Analysis of Physical Properties of Food Materials

The chemical composition of any food material can be analyzed well by employing various analytical techniques. The physical properties of food are no less important than chemical composition as results obtained from authentic measurement data are able to provide detailed information about the food. Several techniques have been used for years for this purpose but most of them are destructive in nature. The aim of this present study is to identify the emerging techniques that have been used by different researchers for the analysis of the physical characteristics of food. It is highly recommended to practice novel methods as these are non-destructive, extremely sophisticated, and provide results closer to true quantitative values. The physical properties are classified into different groups based on their characteristics. The concise view of conventional techniques mostly used to analyze food material are documented in this work.

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.

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.

ASAS-NANP SYMPOSIUM: prospects for interactive and dynamic graphics in the era of data-rich animal science1

Statistical graphics, and data visualization, play an essential but under-utilized, role for data analysis in animal science, and also to visually illustrate the concepts, ideas, or outputs of research and in curricula. The recent rise in web technologies and ubiquitous availability of web browsers enables easier sharing of interactive and dynamic graphics. Interactivity and dynamic feedback enhance human-computer interaction and data exploration. Web applications such as decision support systems coupled with multimedia tools synergize with interactive and dynamic graphics. However, the importance of graphics for effectively communicating data, understanding data uncertainty, and the state of the field of interactive and dynamic graphics is underappreciated in animal science. To address this gap, we describe the current state of graphical methodology and technology that might be more broadly adopted. This includes an explanation of a conceptual framework for effective graphics construction. The ideas and technology are illustrated using publicly available animal datasets. We foresee that many new types of big and complex data being generated in precision livestock farming create exciting opportunities for applying interactive and dynamic graphics to improve data analysis and make data-supported decisions.

Selection for increased visual muscling increases carcass leanness without compromising predicted Meat Standards Australia eating-quality index

Context Selection using visual muscle score (MS) has been proposed to increase carcass leanness (i.e. meat yield), without compromising eating quality. Aims The aim of the present study was to examine the impact that selection for divergent MS has on live animal, commercial carcass and carcass tissue weights by using computed tomography (CT) including Meat Standards Australia (MSA) index-predicted eating quality. Methods Data from 67 steers originating from three muscling lines, namely, low, high and heterozygous high (HighHet – heterozygous for the 821 del11 myostatin mutation), were used. Visual MS was assessed on all steers. All steers were slaughtered and the left-hand side of each carcass was processed with fat trimming limited to only that required for hygiene purposes and kidney fat was not removed. All carcasses were MSA graded and then boned-out into untrimmed boneless primals (e.g. rump, cube roll). A CT scan of each beef primal was processed with image analysis software to estimate lean and fat tissue weights. The following traits were analysed: MS, weaning and slaughter weights; commercial carcass traits, including cold carcass weight, rump fat, MSA rib fat, MSA eye-muscle area, MSA marble score and MSA index; and CT-scanned compositional carcass traits, including lean, fat and bone tissues (%) and lean:bone ratio. All data were analysed with a linear mixed-effects model using REML. Least-squares means for the three muscling lines are reported. Linear trends between MS and seven carcass traits, with and without the myostatin mutation, are presented graphically. Key results Muscling line effects (P &lt; 0.05) were found for visual MS and carcass traits. Linear trends between MS and carcass traits with and without the myostatin mutation demonstrate that increases in MS (P = 0.24) did not compromise predictions of MSA index even though MSA marble score decreased (P = 0.026), but myostatin decreased MSA marble score and tended to decrease MSA index (P = 0.097). Increases in the MSA eye-muscle area were associated with increases in MS (P &lt; 0.01), with little effect of myostatin. Increases in MS and the myostatin mutation were both associated with increases (P &lt; 0.01) in lean tissue (%) and the lean:bone ratio, and decreases (P = 0.02) in fat tissue (%). Conclusions The results indicate selection for high MS can be used to increase carcass yield, without negatively affecting MSA index predictions of eating quality. Implications Producers can use MS to identify animals with higher yields to increase carcass leanness and decrease carcass waste fat, without compromising MSA index predictions of eating quality, but should do so while considering all traits that affect profitability, in particular marble score and its association with eating quality.

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.

Predicting beef carcass composition using tissue weights of a primal cut assessed by computed tomography

The potential of the composition of the forerib measured by X-ray computed tomography (CT) as a predictor of carcass composition was evaluated using data recorded on 30 Aberdeen Angus and 43 Limousin crossbred heifers and steers. The left sides of the carcasses were split into 20 cuts, which were CT scanned and fully dissected into fat, muscle and bone. Carcass and forerib tissue weights were assessed by dissection and CT. Carcass composition was assessed very accurately by CT scanning of the primal cuts (adj-R2 = 0.97 for the three tissues). CT scanning predicted weights of fat, muscle and bone of the forerib with adj-R2 of 0.95, 0.91 and 0.75, respectively. Single regression models with the weights of fat, muscle or bone in the forerib measured by CT as the only predictors to estimate fat, muscle or bone of the left carcass obtained by CT showed adjusted coefficients of determination (adj-R2) of 0.79, 0.60 and 0.52, respectively. By additionally fitting breed and sex, accuracy increased to 0.85, 0.73 and 0.67. Using carcass and forerib weights in addition to the previous predictors improved significantly the prediction accuracy of carcass fat and muscle weights to adj-R2 values of 0.92 and 0.96, respectively, while the highest value for carcass bone weight was 0.77. In general, equations derived using CT data had lower adj-R2 values for bone, but better accuracies for fat and muscle compared to those obtained using dissection. CT scanning could be considered as an alternative very accurate and fast method to assess beef carcass composition that could be very useful for breeding programmes and research studies involving a large number of animals, including the calibration of other indirect methods (e.g. in vivo and carcass video image analysis).