Effects of low protein diets on pigs with a lean genotype 2. Compositional traits measured with computed tomography (CT)

Lambe, N. R.; Wood, JD; McLean, KA; Walling, Grant A.; Whitney, Heather M.; Jagger, S.; Fullarton, P.; Bayntun, J. A.; Bünger, L.

doi:10.1016/j.meatsci.2013.04.038

Cited by 22 publications

(23 citation statements)

References 15 publications

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“…At the 120-kg target slaughter weight, loin weight and bone were predicted much better than at the other target weights, while loin fat was predicted with worst accuracy than at 100 kg. Lambe et al (2013) also found higher R 2 values between dissected and CT-predicted fat, muscle and bone of the carcass side with increasing live weight (60, 85 and 115 kg). Moreover, all the belly parameters estimated in the present study also had higher or similar accuracy at the 120 kg target weight than at the others.…”

Section: Resultsmentioning

confidence: 68%

“…Including physical measurements in the estimation of carcass fat content also reduced the error (CV p between 3.89% and 6.70%). In a recent study, Lambe et al (2013) showed correlation coefficients of 0.53, 0.14 and −0.28 between fat thicknesses measured in CT images and, respectively, fat, muscle and bone weights obtained by dissection. Bone weights for the four main cuts have CT carcass and cut composition of growing pigs been estimated with similar accuracy in all the studied models.…”

Section: Resultsmentioning

confidence: 93%

“…Weight and composition of gain change in pigs during the growing and finishing periods is determined mainly by the genetic potential and supply of nutrients Kouba et al, 1999;Lambe et al, 2013). Knowledge of changes in tissue composition at whole-carcass and primal-cut levels would allow the optimization of slaughter, satisfy niche market demands, and improve the economic efficiency of pork production systems.…”

Section: Introductionmentioning

confidence: 99%

“…However, another method used X-ray computed tomography (CT), a non-destructive and noninvasive technique that measures the density of the tissues and allows the body composition of animals to be estimated easily Barchia et al, 2010;Lambe et al, 2013); thus, serial slaughtering may no longer be required. Nevertheless, serial slaughtering is still needed to establish the relationship between CT data (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of carcass composition and cut composition from computed tomography images of live growing pigs of different genotypes

Font-i-Furnols

Carabús

Pomar³

et al. 2015

Animal

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The aim of the present work was (1) to study the relationship between cross-sectional computed tomography (CT) images obtained in live growing pigs of different genotypes and dissection measurements and (2) to estimate carcass composition and cut composition from CT measurements. Sixty gilts from three genotypes (Duroc × (Landrace × Large White), Pietrain × (Landrace × Large White), and Landrace × Large White) were CT scanned and slaughtered at 30 kg (n = 15), 70 kg (n = 15), 100 kg (n = 12) or 120 kg (n = 18). Carcasses were cut and the four main cuts were dissected. The distribution of density volumes on the Hounsfield scale (HU) were obtained from CT images and classified into fat (HU between −149 and −1), muscle (HU between 0 and 140) or bone (HU between 141 and 1400). Moreover, physical measurements were obtained on an image of the loin and an image of the ham. Four different regression approaches were studied to predict carcass and cut composition: linear regression, quadratic regression and allometric equations using volumes as predictors, and linear regression using volumes and physical measurements as predictors. Results show that measurements from whole animal taken in vivo with CT allow accurate estimation of carcass and cut composition. The prediction accuracy varied across genotypes, BW and variable to be predicted. In general, linear models, allometric models and linear models, which included also physical measurements at the loin and the ham, produced the lowest prediction errors.

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Section: Resultsmentioning

confidence: 68%

Section: Resultsmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of carcass composition and cut composition from computed tomography images of live growing pigs of different genotypes

Font-i-Furnols

Carabús

Pomar³

et al. 2015

Animal

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“…Each device typically includes its own software, but researchers usually require more information than that generally provided. Software used in published works include Visual Pork (Bardera et al, 2012), ATAR (Animal Tomogram Analysis Routines)-STAR (Lambe et al, 2013), Osirix (Rosset et al, 2004), Dicom Works 1.3.5, and Lunar 4.7e (Kremer et al, 2013). Images can be analysed in three different ways: using phenotypic measurements, such as linear measurements, areas or volumes; using segmentation based on the application of algorithms to classify each voxel according to its density or signal intensity; or using the volume distribution by HU value or signal intensity.…”

Section: Third Step Image Analysis Proceduresmentioning

confidence: 99%

Imaging technologies to study the composition of live pigs: A review

Carabús¹,

Gispert²,

Font-i-Furnols³

2016

Span. j. agric. res.

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Image techniques are increasingly being applied to livestock animals. This paper overviews recent advances in image processing analysis for live pigs, including ultrasound, visual image analysis by monitoring, dual-energy X-ray absorptiometry, magnetic resonance imaging and computed tomography. The methodology for live pigs evaluation, advantages and disadvantages of different devices, the variables and measurements analysed, the predictions obtained using these measurements and their accuracy are discussed in the present paper. Utilities of these technologies for livestock purposes are also reviewed. Computed tomography and magnetic resonance imaging yield useful results for the estimation of the amount of fat and lean mass either in live pigs or in carcasses. Ultrasound is not sufficiently accurate when high precision in estimating pig body composition is necessary but can provide useful information in agriculture to classify pigs for breeding purposes or before slaughter. Improvements in factors, such as the speed of scanning, cost and image accuracy and processing, would advance the application of image processing technologies in livestock animals.

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X‐Ray Computed Tomography Meets Robust Chemometric Latent Space Modeling for Lean Meat Percentage Prediction in Pig Carcasses

Mishra,

Font‐i‐Furnols

2024

Journal of Chemometrics

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This study presents a case of processing X‐ray computed tomography (CT) data for pork scans using chemometric latent space modeling. The distribution of voxel intensities is shown to exemplify a multivariate, multi‐collinear signal mixture. While this concept is not novel, it is revisited here from a chemometric perspective. To extract meaningful information from such multivariate signals, latent space modeling based on partial least squares (PLS) is an ideal solution. Furthermore, a robust PLS approach is even more effective for latent space modeling, as it can extract latent spaces unaffected by outliers, thereby enhancing predictive modeling. As an example, lean meat percentage is predicted using X‐ray CT data and robust PLS regression. This method is applicable to X‐ray CT quantification analysis, particularly in cases where unclear, erroneous, and outlying observations are suspected in the data.

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Effects of low protein diets on pigs with a lean genotype 2. Compositional traits measured with computed tomography (CT)

Cited by 22 publications

References 15 publications

Estimation of carcass composition and cut composition from computed tomography images of live growing pigs of different genotypes

Estimation of carcass composition and cut composition from computed tomography images of live growing pigs of different genotypes

Imaging technologies to study the composition of live pigs: A review

X‐Ray Computed Tomography Meets Robust Chemometric Latent Space Modeling for Lean Meat Percentage Prediction in Pig Carcasses

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