Objective carcass measurement technologies: Latest developments and future trends

Delgado‐Pando, Gonzalo; Allen, Paul; Troy, Declan J.; McDonnell, Ciara K.

doi:10.1016/j.tifs.2020.12.016

Cited by 32 publications

(14 citation statements)

References 76 publications

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“…Although 2D shape information can be useful, 3D information is preferable to ensure more accurate weight estimates [ 35 ] and, therefore, better carcass quality prediction. There is a growing interest in developing prediction models of carcass and meat quality traits using 3D measurements, for example, computed tomography (CT) and other image-based approaches [ 5 , 7 ]. With CT, most works target pigs [ 36 , 37 ].…”

Section: Resultsmentioning

confidence: 99%

“…In general, these works aimed to classify the carcass quality based on non-destructive image analysis techniques by introducing consistency, accuracy, credibility, and confidence in the value assessment of the carcass [ 1 , 6 ]. The ultimate aim is to replace the subjective evaluation grounded on standards and move towards advanced value-based payment systems [ 7 ]. These evaluations techniques are also relevant for the classification of light lamb carcasses produced in Mediterranean countries.…”

Section: Introductionmentioning

confidence: 99%

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

Silva

Almeida

Condotta

et al. 2021

Animals

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Silva

Almeida

Condotta

et al. 2021

Animals

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“…The subcutaneous fat layer is almost "invisible" (very thin) in Pietrain, while Large Black deposits significant amounts of subcutaneous and visceral fat. Even the outer shapes of the bodies of both breeds differ widely, which can be used for 3D (2D) video imaging [8,9,16]. Large Black shows a more circular body shape, while Pietrain looks more quadratic or rectangular with a slight ridge at the middle line of the back (spine), which is related to the significant difference when comparing the volume and the shape of the longissimus muscle (measured as loin eye area in cm 2 or loin eye volume in cm 3 ).…”

Section: Principle Of Magnetic Resonance Imaging: the Body Part Of Interest Is Positioned In The Center Of The Magnetic Field While A Bodmentioning

confidence: 99%

“…In recent years, several publications reviewed (noninvasive) methods for determining the body composition of livestock, especially pigs [12][13][14][15]. A comprehensive review of the methods applied or being tested for carcass grading and meat quality determination in livestock has been published recently by Delgado-Pando et al [16]. Therefore, this review will mainly focus on the latest developments of noninvasive methods to study body composition, health, growth, and efficiency of pigs in vivo.…”

Section: Introductionmentioning

confidence: 99%

Tracing the Inside of Pigs Non-Invasively: Recent Developments

Scholz¹,

Kušec²,

Mitchell³

et al. 2024

Tracing the Domestic Pig

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Regional markets require a large variety of pig breeds and pork products. Noninvasive techniques like computed tomography, magnetic resonance imaging, dual-energy X-ray absorptiometry, computer vision, or, very often, ultrasound helps to provide the information required for breeding, quality control, payment, and processing. Meanwhile, computed tomography is being used as phenotyping tool by leading pig breeding organizations around the world, while ultrasound B- or A-mode techniques belong to the standard tools, especially to measure subcutaneous fat and muscle traits. Magnetic resonance imaging and dual-energy X-ray absorptiometry, however, are still mainly used as research tools to develop and characterize new phenotypic traits, which usually could not be measured without slaughtering the breeding pigs. A further noninvasive method—already used on a commercial basis, not only in abattoirs—is video 2D or 3D imaging. This chapter will review the latest developments for these noninvasive techniques.

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“…For this reason, more objective tools for classifying beef carcasses have been developed in recent years [3][4][5]. These include instrumental methods such as ultrasound [6][7][8][9], computed tomography [10,11], dual energy X-ray absorptiometry (DEXA) [12,13], a bioelectrical impedance analysis (BIA) [14], near-infrared spectroscopy [15,16], microwave systems [17] and, above all, an image analysis [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Predicting Beef Carcass Fatness Using an Image Analysis System

Mendizábal

Ripoll

Urrutia

et al. 2021

Animals

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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.

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Objective carcass measurement technologies: Latest developments and future trends

Cited by 32 publications

References 76 publications

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

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

Tracing the Inside of Pigs Non-Invasively: Recent Developments

Predicting Beef Carcass Fatness Using an Image Analysis System

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