The application of digital imaging techniques in the in vivo estimation of the body composition of pigs: a review

Szabó, Csaba; Babinszky, L.; Verstegen, M.W.A.; Vangen, O.; Jansman, A.J.M.; Kanis, E.

doi:10.1016/s0301-6226(99)00050-0

Cited by 56 publications

(36 citation statements)

References 23 publications

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“…Indirect methods are preferred for determining animal nutritional requirements or for studying the effect of nutrition on animal growth. However, indirect methods require calibration to accurately estimate body composition (Szabo et al, 1999;Mitchell et al, 2003;Mercier et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Use of dual-energy x-ray absorptiometry in non-ruminant nutrition research

2017

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-Precise body composition measurements are essential in animal nutrition studies because the impact of treatments is evaluated based on changes in body weight and composition. Various indirect techniques for animal compositional evaluation have been developed and evaluated for applicability in animal nutrition studies. A fast, accurate, minimally invasive method that requires little input is considered the ideal for providing information about the animal. Measurements obtained by dual-energy x-ray absorptiometry (DXA) are highly correlated with those obtained by chemical analysis and dissection. The algorithms of DXA software partition the six chemical components of the body (lipids, water, proteins, carbohydrates, non-bone mineral, and bone mineral) into three compartments (total body mineral content, fat mass, and lean mass). Questions have been raised about how this partitioning affects the precision of the DXA method. In addition, the relationship between the DXA measurements and dissected carcass tissues is nonrepresentational of the relationship between DXA and chemical analysis. Furthermore, since DXA devices and their software were developed primarily for human medicine, they may not be fully adequate for animal evaluation. Calibration is required to obtain true values. The DXA method has some advantages and disadvantages that should be identified and controlled before calibration. Nonetheless, DXA is a valuable tool that provides precise, repeatable body composition measurements of live monogastric animals and their carcasses.

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Section: Introductionmentioning

confidence: 99%

Use of dual-energy x-ray absorptiometry in non-ruminant nutrition research

2017

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“…The carcass lean content is calculated from backfat thickness and longissimus muscle depth between the third and fourth last ribs, and these measurements are then converted into an estimate value of lean meat content by the respective equipment (Ministère des Classes Moyennes et de l 'Agricultur e , 1999). The correlation between backfat thickness, longissimus muscle depth and longissimus muscle area measured on carcasses, and those corresponding to live animal measurements done by real-time ultrasound, are high with good repeatability (McLaren et al, 1991;Fischer, 1997;Szabo et al, 1999). Ultrasonic techniques therefore offer the possibility of predicting carcass composition when animals are scanned in vivo (Sather et al, 1996 ;Szabo et al, 1999;Hulsegge et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

Prediction of carcass lean content by real-time ultrasound in Pietrain and negative stress Pietrain

Karim¹,

Verleyen

Leroy

2002

Anim. Sci.

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lean proportion was higher in homozygote TT than homozygote CC and heterozygote CT individuals ( P < 0·05). Gilts had more lean meat than barrows ( P < 0·05). The correlation between the lean meat proportion and ultrasound backfat thickness (UBFT) or ultrasound longissimus muscle depth (ULMD) respectively was moderate.The prediction of lean meat proportion using UBFT, ULMD and ULMA gave an R 2 which varied from 0·35 to 0·79. Real-time ultrasound is a tool that could potentially be used to predict the composition of pig carcasses before slaughter particularly if measurements can be taken with a higher degree of accuracy than at present.

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“…There have been very few studies on the estimation of the pig chemical composition using CT technology. Szabo et al (1999) showed R 2 values for the fat (0.89), protein (0.83) and water (0.82) content in live animals, although they did not present prediction error values. The lower accuracy for protein can be explained by the narrow Pig chemical composition with computed tomography range of variation of the protein content in the present study in accordance with Prieto et al (2009) in NIRS, and also by analytical differences between the Kjeldahl methodology and CT.…”

Section: Discussionmentioning

confidence: 91%

“…Computed tomography (CT) is a non-invasive technology that is based on the attenuation of X-rays as they pass through body tissues. Previous studies have proven that CT is a suitable method to estimate body composition in live pigs (Szabo et al, 1999;Font-i-Furnols et al, 2015) and carcasses (Font i Furnols et al, 2009). Szabo et al (1999) and Arthur et al (2011) showed the potential of this technology to measure the main chemical body components, but information on the relationship between CT and chemical composition is still limited.…”

Section: Introductionmentioning

confidence: 99%

Predicting the carcass chemical composition and describing its growth in live pigs of different sexes using computed tomographys

Zomeño

Gispert

Carabús

et al. 2016

Animal

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The aims of this study were (1) to evaluate the ability of computed tomography (CT) to predict the chemical composition of live pigs and carcasses, (2) to compare the chemical composition of four different sex types at a commercial slaughter weight and (3) to model and evaluate the chemical component growth of these sex types. A total of 92 pigs (24 entire males (EM), 24 surgically castrated males (CM), 20 immunocastrated males (IM) and 24 females (FE)) was used. A total of 48 pigs (12 per sex type) were scanned repeatedly in vivo using CT at 30, 70, 100 and 120 kg and slaughtered at the end of the experiment. The remaining 44 were CT scanned in vivo and slaughtered immediately: 12 pigs (4 EM, 4 CM and 4 FE) at 30 kg and 16 pigs each at 70 kg and 100 kg (4 per sex type). The left carcasses were CT scanned, and the right carcasses were minced and analysed for protein, fat, moisture, ash, Ca and P content. Prediction equations for the chemical composition were developed using Partial Least Square regression. Allometric growth equations for the chemical components were modelled. By using live animal and carcass CT images, accurate prediction equations were obtained for the fat (with a root mean square error of prediction (RMSEP CV ) of 1.31 and 1.34, respectively, and R 2 = 0.91 for both cases) and moisture relative content (g/100 g) (RMSEP CV = 1.19 and 1.38 and R 2 = 0.94 and 0.93, respectively) and were less accurate for the protein (RMSEP CV = 0.65 and 0.67 and R 2 = 0.54 and 0.63, respectively) and mineral content (RMSEP CV from 0.28 to 1.83 and R 2 from 0.09 to 0.62). Better equations were developed for the absolute amounts of protein, fat, moisture and ash (kg) (RMSEP CV from 0.26 to 1.14 and R 2 from 0.91 to 0.99) as well as Ca and P (g) (RMSEP CV = 144 and 71, and R 2 = 0.76 to 0.66, respectively). At 120 kg, CM had a higher fat and lower moisture content than EM. For protein, CM and IM had lower values than FE and EM. The ash content was higher in EM and IM than in FE and CM, while IM had a higher Ca and P content than the others. The castrated animals showed a higher allometric coefficient for fat and a lower one for moisture, with IM having intermediate values. However, for the Ca and P models, IM presented higher coefficients than EM and FE, and CM were intermediate.

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The application of digital imaging techniques in the in vivo estimation of the body composition of pigs: a review

Cited by 56 publications

References 23 publications

Use of dual-energy x-ray absorptiometry in non-ruminant nutrition research

Use of dual-energy x-ray absorptiometry in non-ruminant nutrition research

Prediction of carcass lean content by real-time ultrasound in Pietrain and negative stress Pietrain

Predicting the carcass chemical composition and describing its growth in live pigs of different sexes using computed tomographys

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