Hostname: page-component-76fb5796d-22dnz Total loading time: 0 Render date: 2024-04-26T07:44:44.494Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  28 August 2015

C. Zomeño ,
M. Gispert ,
A. Carabús ,
A. Brun  and
M. Font-i-Furnols
C. Zomeño
Affiliation:
IRTA-Product Quality, Finca Camps i Armet, E-17121 Monells, Catalonia, Spain
M. Gispert
Affiliation:
IRTA-Product Quality, Finca Camps i Armet, E-17121 Monells, Catalonia, Spain
A. Carabús
Affiliation:
IRTA-Product Quality, Finca Camps i Armet, E-17121 Monells, Catalonia, Spain
A. Brun
Affiliation:
IRTA-Product Quality, Finca Camps i Armet, E-17121 Monells, Catalonia, Spain
M. Font-i-Furnols*
Affiliation:
IRTA-Product Quality, Finca Camps i Armet, E-17121 Monells, Catalonia, Spain
*
†E-mail: maria.font@irta.cat
Get access

Abstract

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 (RMSEPCV) of 1.31 and 1.34, respectively, and R2=0.91 for both cases) and moisture relative content (g/100 g) (RMSEPCV=1.19 and 1.38 and R2=0.94 and 0.93, respectively) and were less accurate for the protein (RMSEPCV=0.65 and 0.67 and R2=0.54 and 0.63, respectively) and mineral content (RMSEPCV from 0.28 to 1.83 and R2 from 0.09 to 0.62). Better equations were developed for the absolute amounts of protein, fat, moisture and ash (kg) (RMSEPCV from 0.26 to 1.14 and R2 from 0.91 to 0.99) as well as Ca and P (g) (RMSEPCV=144 and 71, and R2=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.

Keywords

computed tomographychemical compositionprediction equationspig growthimmunocastration
Type
Research Article
Information
animal , Volume 10 , Supplement 1 , January 2016 , pp. 172 - 181
Copyright
© The Animal Consortium 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Arthur, PF, Barchia, IM, Giles, LR and Eamens, GJ 2011. Chemical composition of growing pigs and its relationship with body tissue composition assessed by X-ray-computed tomography. Journal of Animal Science 89, 3935–3944.Google Scholar
Blasco, A 2001. The Bayesian controversy in animal breeding. Journal of Animal Science 79, 2023–2046.Google Scholar
Blasco, A 2005. The use of Bayesian statistics in meat quality analyses: a review. Meat Science 69, 115–122.CrossRefGoogle ScholarPubMed
Blasco, A 2011. La significación es irrelevante y los P-valores engañosos. Qué hacer? ITEA 107, 48–58.Google Scholar
Blasco, A 2012. The program “rabbit”. Retrieved July 2, 2015, from http://www.dcam.upv.es/dcia/ablasco/Programas/THE%20PROGRAM%20Rabbit.pdf Google Scholar
Bünger, L, Clelland, N, Moore, K, McLean, K, Kongsro, J and Lambe, N 2014. Integrating computed tomography (CT) into commercial sheep breeding in the UK: cost and value. In Farm animal imaging Copenhagen 2014 (ed. CA Maltin, C Craigie and L Bünger), pp. 22–27. SRUC, Edinburgh, UK.Google Scholar
Carabús, A, Sainz, RD, Oltjen, JW, Gispert, M and Font-i-Furnols, M 2015. Growth of total fat and lean and the primal cuts in relation to estimated mature weight in pigs of different sexual conditions, assessed using computed tomography. Animal 93, 1388–1397.Google Scholar
Causeur, D, Daumas, G, Dhorne, T, Engel, B, Font i Furnols, M and Højsgaard, S 2003. Statistical Handbook for assessing pig classification methods. Recommendations from the “EUPIGCLASS” project group. Retrieved November 12, 2013, from http://ec.europa.eu/agriculture/pigmeat/policy-instruments/statistical-handbook-forassessing-pig-classification-methods_en.pdf Google Scholar
de Lange, CFM, Morel, PCH and Birkett, SH 2003. Modeling chemical and physical body composition of the growing pig. Journal of Animal Science 81, 159–165.Google Scholar
Fàbrega, E, Velarde, A, Cros, J, Gispert, M, Suárez, P, Tibau, J and Soler, J 2010. Effect of vaccination against gonadotrophin-releasing hormone, using Improvac®, on growth performance, body composition, behaviour and acute phase proteins. Livestock Science 132, 53–59.Google Scholar
Font-i-Furnols, M, Carabús, A, Pomar, C and Gispert, M 2015. Estimation of carcass and cuts composition from computed tomography images of growing live pigs of different genotypes. Animal 9, 166–178.CrossRefGoogle ScholarPubMed
Font i Furnols, M, Teran, F and Gispert, M 2009. Estimation of lean meat content in pig carcasses using X-ray computed tomography and PLS regression. Chemometrics and Intelligent Laboratory Systems 98, 31–37.Google Scholar
Gispert, M, Oliver, MA, Velarde, A, Suarez, P, Pérez, J and Font-i-Furnols, M 2010. Carcass and meat quality characteristics of immunocastrated male, surgically castrated male, entire male and female pigs. Meat Science 85, 664–670.Google Scholar
Gould, SJ 1971. Geometric similarity in allometric growth: a contribution to the problem of scaling in the evolution of size. The American Naturalist 105, 113–136.Google Scholar
Konsgro, J 2014. Genetic gain on body composition in pigs by computed tomography (CT): return on investment. In Farm animal imaging Copenhagen 2014 (ed. CA Maltin, C Craigie and L Bünger), pp. 28–30. SRUC, Edinburgh, UK.Google Scholar
Noblet, J, Karege, C, Dubois, S and van Milgen, J 1999. Metabolic utilization of energy and maintenance requirements in growing pigs: effects of sex and genotype. Journal of Animal Science 77, 1208–1216.CrossRefGoogle ScholarPubMed
Prieto, N, Roehe, R, Lavín, P, Batten, G and Andrés, S 2009. Application of near infrared reflectance spectroscopy to predict meat and meat products quality: a review. Meat Science 83, 175–186.Google Scholar
Schinckel, AP, Mahan, DC, Wiseman, TG and Einstein, ME 2008. Growth of protein, moisture, lipid, and ash of two genetic lines of barrows and gilts from twenty to one hundred twenty-five kilograms of body weight. Journal of Animal Science 86, 460–471.Google Scholar
Szabo, C, Babinszky, L, Verstegen, MWA, Vangen, O, Jansman, AJM and Kanis, E 1999. The application of digital imaging techniques in the in vivo estimation of the body composition of pigs: a review. Livestock Production Science 60, 1–11.CrossRefGoogle Scholar
Williams, PC 2001. Implementation of near-infrared technology. In Near-infrared technology in the agricultural and food industries (ed. PC Williams and K Norris), pp. 145–169. American Association of Cereal Chemists, St. Paul, MN, USA.Google Scholar
Supplementary material: File

Zomeño supplementary material

Table S1

Download Zomeño supplementary material(File)
File 57.9 KB