Relationships among carcass shape, tissue composition, primal cuts and meat quality traits in lambs: A PLS path modeling approach

Araújo, Jamille Carla Oliveira; Lima, Alyne Cristina Sodré; Nunes, Mário Pinharanda; Sousa, Maria Alice Montes de; Serrão, Gabriel Xavier; Morais, Emerson Cordeiro; Daher, Luciara Celi Chaves; Silva, A.G.M.

doi:10.1016/j.smallrumres.2019.106024

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…Apart from carcass quality estimations, CVS have also been used to predict meat quality. In this context, Araújo et al (2020) evaluated the relationships between carcass shape, carcass tissue characterization, and commercial cuts of hair sheep lambs to predict meat quality (R 2 p = 0.82), suggesting the potential of the technology to establish categories for carcass classification in lambs. In fact, Stewart et al (2021) observed accurate predictions for rib-eye area (R 2 = 0.83), Meat Standards Australia marbling (R 2 = 0.76), AUS-MEAT marbling (R 2 = 0.70), and chemical IMF (R 2 = 0.78) using a prototype vision system on a phenotypically diverse beef and lamb carcass population.…”

Section: Computer Vision Systemsmentioning

confidence: 99%

Prediction of Carcass Composition and Meat and Fat Quality Using Sensing Technologies: A Review

Leighton

Marcoux

Wei

et al. 2022

Meat and Muscle Biology

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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.

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Section: Computer Vision Systemsmentioning

confidence: 99%

Prediction of Carcass Composition and Meat and Fat Quality Using Sensing Technologies: A Review

Leighton

Marcoux

Wei

et al. 2022

Meat and Muscle Biology

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“…In Alcayde et al [10] a custombuilt platform was used to identify pork meat age using image analysis and three different regression algorithms. The quality analysis of lamb carcasses using videos and a computer vision system has been investigated by Araújo et al [11], with positive results.…”

Section: Introductionmentioning

confidence: 99%

Beef Carcass Grading using Deep Convolutional Networks

Menezes,

Tetila,

Gonçalves

et al. 2023

Anais Do XVIII Workshop De Visão Computacional (WVC 2023)

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Beef carcass grading is an invaluable tool to ensure meat quality. In most of the Brazilian abattoirs, carcasses are graded through visual analysis by trained graders. In order to automate this process, we evaluate seven image deep learning models. For this purpose, a new dataset was created containing images of 670 bovine half-carcasses taken during regular operation in an abattoir. The images were graded by three professionals. All three experts agreed in only 9.9% of the cases, and two out of three graders agreed in 58.82%. The graders disagreed on 31.28% of the images. These results indicate the complexity of the problem. Nonetheless, an overall accuracy of 53% was achieved using convolutional neural networks, which is close to human performance, when the agreement between the graders is considered. Furthermore, an accuracy of around 91% can be achieved if the cases of disagreement are disregarded.

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“…The lack of simple, non-destructive, rapid and reliable methods to assess carcass classification and the characteristics of carcass joints has been one of the barriers to developing quality control systems in the meat industry [ 5 , 6 ]. To overcome these difficulties, several efforts have been employed to develop fast, simple, objective and inexpensive methods of establishing measurements of the carcass and its tissues or cuts [ 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Usually, the works published on VIA apply to sheep use as far as possible. The carcass spectrum of lambs slaughtered in different production systems typically ranges in weight between 15 and 30 kg [ 7 , 8 , 14 ]. However, there is a lack of information for light carcasses, and in that regard, this study aims to evaluate the accuracy of a flexible, low-cost VIA system in predicting the weight and yield of lean, commercial cuts from light lamb carcasses.…”

Section: Introductionmentioning

confidence: 99%

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

Batista

Santos

Afonso³

et al. 2021

Animals

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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.

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Relationships among carcass shape, tissue composition, primal cuts and meat quality traits in lambs: A PLS path modeling approach

Cited by 8 publications

References 34 publications

Prediction of Carcass Composition and Meat and Fat Quality Using Sensing Technologies: A Review

Prediction of Carcass Composition and Meat and Fat Quality Using Sensing Technologies: A Review

Beef Carcass Grading using Deep Convolutional Networks

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

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