Journal of Food Measurement and Characterization

, Volume 13, Issue 3, pp 2230–2240 | Cite as

The prediction of lean meat and subcutaneous fat with skin content in pork cuts on the carcass meatness and weight

  • Vladimir TomovićEmail author
  • Lato Pezo
  • Marija Jokanović
  • Mila Tomović
  • Branislav Šojić
  • Snežana Škaljac
  • Dragan Vujadinović
  • Maja Ivić
  • Ilija Djekić
  • Igor Tomašević
Original Paper


Early post-mortem, objective and non-destructive prediction of tissue distribution in the major pork cuts is a challenge for the meat industry. Mathematical models to predict pig carcass composition using total lean meat percentage and carcass weight were evaluated in this study. The data were obtained from 455 cold pig carcasses which were dissected according to the EU reference method; total lean meat percentage and carcass weight ranged from 42.45 to 69.21% and from 23.26 to 55.22 kg, respectively. Developed empirical models gave a reasonable fit to the experimental data and successfully predicted the carcass composition and tissue distribution in primal cuts. The second order polynomial models showed high coefficients of determination for prediction of experimental results (between 0.612 and 0.929), while the artificial neural network (ANN) model, based on the Broyden–Fletcher–Goldfarb–Shanno iterative algorithm, showed better prediction capabilities (overall r2 was 0.889). The newly developed software, based on ANN model is easy, fast, cheap and with sufficient precision for application in the meat industry.


Pig Carcass composition Tissue distribution Meatiness Fatness Mathematical modelling 



Research was financially supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Project TR31032.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Council Regulation (EC) No 3220/84 of 13 November 1984 determining the Community scale for grading pig carcass, Off. J. Eur. Comm. L301, 1Google Scholar
  2. 2.
    Regulation (EU) No 1308/2013 of the European Parliament and of the Council of 17 December 2013 establishing a common organisation of the markets in agricultural products and repealing Council Regulations (EEC) No 922/72, (EEC) No 234/79, (EC) No 1037/2001 and (EC) No 1234/2007. Off. J. Eur. Comm. L347, 671Google Scholar
  3. 3.
    Commission Regulation (EC) No 1249/2008 of 10 December 2008 laying down detailed rules on the implementation of the Community scales for the classification of beef, pig and sheep carcases and the reporting of prices thereof. Off. J. Eur. Comm. L337, 3Google Scholar
  4. 4.
    Commission Regulation (EC) No 3127/94 of 20 December 1994 amending Regulation (EC) No 2967/85 laying down detailed rules for the application of the Community scale for grading pig carcases. Off. J. Eur. Comm. No L330, 43Google Scholar
  5. 5.
    P. Walstra, G.S.M. Merkus, Procedure for assessment of the lean meat percentage as a consequence of the new EU reference dissection method in pig carcass classification. (DLO—Institute for Animal Science and Health—Report ID-DLO 96.014, Zeist, 1996)Google Scholar
  6. 6.
    Commission Decision (2004/370/EC) of 15 April 2004 authorising methods for grading pig carcases in the United Kingdom. Off. J. Eur. Comm. L116, 1Google Scholar
  7. 7.
    M. Font-i-Furnols, M. Gispert, Meat Sci. 83, 443 (2009)CrossRefGoogle Scholar
  8. 8.
    M. Font-i-Furnols, M. Čandek-Potokar, G. Daumas, M. Gispert, M. Judas, M. Seynaeve, Meat Sci. 113, 1 (2016)CrossRefGoogle Scholar
  9. 9.
    J.D. Gresham, S.R. McPeake, J.K. Bernard, H.H. Henderson, J. Anim. Sci. 70, 631 (1992)CrossRefGoogle Scholar
  10. 10.
    A.D. Mitchell, J.M. Conway, W.J.E. Potts, J. Anim. Sci. 74, 2663 (1996)CrossRefGoogle Scholar
  11. 11.
    M. Marcoux, J.F. Bernier, C. Pomar, Meat Sci. 63, 359 (2003)CrossRefGoogle Scholar
  12. 12.
    M. Marcoux, L. Faucitano, C. Pomar, Meat Sci. 70, 655 (2005)CrossRefGoogle Scholar
  13. 13.
    A.M. Scholz, M. Förster, Arch. Tierz. Dummerstorf 49, 462 (2006)Google Scholar
  14. 14.
    A.D. Mitchell, A.M. Scholz, V.G. Pursel, C.M. Evock-Clover, J. Anim. Sci. 76, 2104 (1998)CrossRefGoogle Scholar
  15. 15.
    A.D. Mitchell, A.M. Scholz, P.C. Wang, H. Song, J. Anim. Sci. 79, 1800 (2001)CrossRefGoogle Scholar
  16. 16.
    U. Baulain, Comput. Electron. Agric. 17, 189 (1997)CrossRefGoogle Scholar
  17. 17.
    U. Baulain, M. Friedrichs, R. Höreth, M. Henning, E. Tholen, Use of MRI to assess carcass and primal cut composition in different pig breeds, Accessed 24 Jan 2019
  18. 18.
    D. Lisiak, K. Duziński, P. Janiszewski, K. Borzut, D. Knecht, Anim. Prod. Sci. 55, 1044 (2014)CrossRefGoogle Scholar
  19. 19.
    M. Bernau, P.V. Kremer, E. Lauterbach, E. Tholen, B. Petersen, E. Pappenberger, A.M. Scholz, Meat Sci. 104, 58 (2015)CrossRefGoogle Scholar
  20. 20.
    M. Monziols, G. Collewet, F. Mariette, M. Kouba, A. Davenel, Magn. Reson. Imaging 23, 745 (2005)CrossRefGoogle Scholar
  21. 21.
    M. Monziols, G. Collewet, M. Bonneau, F. Mariette, A. Davenel, M. Kouba, Meat Sci. 72, 146 (2006)CrossRefGoogle Scholar
  22. 22.
    E.P. Berg, B.A. Engel, J.C. Forrest, J. Anim. Sci. 76, 18 (1998)CrossRefGoogle Scholar
  23. 23.
    K. Swensen, M. Ellis, M.S. Brewer, J. Novakofski, F.K. McKeith, J. Anim. Sci. 76, 2405 (1998)CrossRefGoogle Scholar
  24. 24.
    K. Swensen, M. Ellis, M.S. Brewer, J. Novakofski, F.K. McKeith, J. Anim. Sci. 76, 2399 (1998)CrossRefGoogle Scholar
  25. 25.
    M. Gispert, P. Gou, A. Diestre, Food Chem. 69, 457 (2000)CrossRefGoogle Scholar
  26. 26.
    A.P. Schinckel, J.R. Wagner, J.C. Forrest, M.E. Einstein, J. Anim. Sci. 79, 1093 (2001)CrossRefGoogle Scholar
  27. 27.
    E.K. McClure, J.A. Scanga, K.E. Belk, G.C. Smith, J. Anim. Sci. 81, 1193 (2003)CrossRefGoogle Scholar
  28. 28.
    J. Díez, A. Bahamonde, J. Alonso, S. López, J.J. del Coz, J.R. Quevedo, J. Ranilla, O. Luaces, I. Alvarez, L.J. Royo, F. Goyache, Meat Sci. 64, 249 (2003)CrossRefGoogle Scholar
  29. 29.
    I. Hatem, J. Tan, D.E. Gerrard, Meat Sci. 65, 999 (2003)CrossRefGoogle Scholar
  30. 30.
    J. Lu, J. Tan, P. Shatadal, D.E. Gerrard, Meat Sci. 56, 57 (2000)CrossRefGoogle Scholar
  31. 31.
    H. Hwang, B. Park, M. Nguyen, Y.R. Chen, Comput. Electron. Agr. 17, 281 (1997)CrossRefGoogle Scholar
  32. 32.
    C. Borggaard, N. Madsen, H. Thodberg, Meat Sci. 43, 151 (1996)CrossRefGoogle Scholar
  33. 33.
    J. Čítek, R. Stupka, M. Okrouhlá, K. Vehovský, L. Stádník, D. Němečková, M. Šprysl, Ann. Anim. Sci. 15, 1009 (2015)CrossRefGoogle Scholar
  34. 34.
    L.P. Johnson, J.O. Reagan, K.D. Haydon, M.F. Miller, J. Anim. Sci. 68, 4176 (1990)CrossRefGoogle Scholar
  35. 35.
    M. Prevolnik, D. Škorjanc, M. Čandek-Potokar, M. Novič, in Artificial Neural Networks—Industrial and Control Engineering Applications, ed. by K. Suzuki (InTech, Rijeka, 2011), p. 223Google Scholar
  36. 36.
    Y. Chen, K. Cai, Z. Tu, W. Nie, T. Ji, B. Hu, C. Chena, S. Jianga, J. Sci. Food Agric. 98, 3022 (2018)Google Scholar
  37. 37.
    A.M. Peres, L.G. Dias, M. Joy, A. Teixeira, J. Anim. Sci. 88, 572 (2010)CrossRefGoogle Scholar
  38. 38.
    J.G. Ibarra, Y. Tao, H.W. Xin, Opt. Eng. 39, 3032 (2000)CrossRefGoogle Scholar
  39. 39.
    Council Regulation (EC) No 1234/2007 of 22 October 2007 establishing a common organisation of agricultural markets and on specific provisions for certain agricultural products (Single CMO Regulation). Off. J. Eur. Comm. L299, 1Google Scholar
  40. 40.
    StatSoft Inc. STATISTICA (data analysis software system). Version 10.0.
  41. 41.
    G.E.P. Box, D.W. Behnken, Technometrics 2, 455 (1960)CrossRefGoogle Scholar
  42. 42.
    A.I. Khuri, S. Mukhopadhyay, Wiley Interdiscip. RevComput. Stat. 2, 128 (2010)CrossRefGoogle Scholar
  43. 43.
    X. Hu, Q. Weng, Remote Sens. Environ. 113, 2089 (2009)CrossRefGoogle Scholar
  44. 44.
    S. Karlović, T. Bosiljkov, M. Brnčić, D. Ježek, B. Tripalo, F. Dujmić, I. Džineva, A. Skupnjak, Bulg. J. Agric. Sci. 19, 1372 (2013)Google Scholar
  45. 45.
    S. Grieu, O. Faugeroux, A. Traoré, B. Claudet, J.L. Bodnar, Energ. Buildings 43, 543 (2011)CrossRefGoogle Scholar
  46. 46.
    L.L. Pezo, B.Lj Ćurčić, V.S. Filipović, M.R. Nićetin, G.B. Koprivica, N.M. Mišljenović, Lj B. Lević, Hem. Ind. 67, 465 (2013)Google Scholar
  47. 47.
    T. Kollo, D. von Rosen, Advanced Multivariate Statistics with Matrices (Springer, Dordrecht, 2005)CrossRefGoogle Scholar
  48. 48.
    I.C. Trelea, A.L. Raoult-Wack, G. Trystram, Food Sci. Technol. Int. 3, 459 (1997)CrossRefGoogle Scholar
  49. 49.
    J.J. Montaño, A. Palmer, Neural Comput. Appl. 12, 119 (2003)CrossRefGoogle Scholar
  50. 50.
    B.J. Taylor, Methods and Procedures for the Verification and Validation of Artificial Neural Networks (Springer, New York, 2006)Google Scholar
  51. 51.
    M. Arsenović, L. Pezo, S. Stanković, Z. Radojević, Appl. Clay Sci. 115, 108 (2015)CrossRefGoogle Scholar
  52. 52.
    I.A. Basheer, M. Hajmeer, J. Microbiol. Methods 43, 3 (2000)CrossRefGoogle Scholar
  53. 53.
    P.B. Chattopadhyay, R. Rangarajan, Agric. Water Manag. 133, 81 (2014)CrossRefGoogle Scholar
  54. 54.
    D.C. Montgomery, Design and Analysis of Experiments, 2nd edn. (Wiley, New York, 1984)Google Scholar
  55. 55.
    P.S. Madamba, LWT - Food Sci. Technol. 35, 584 (2002)CrossRefGoogle Scholar
  56. 56.
    T. Turanyi, A.S. Tomlin, Analysis of Kinetics Reaction Mechanisms (Springer, Berlin, 2014)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Vladimir Tomović
    • 1
    Email author
  • Lato Pezo
    • 2
  • Marija Jokanović
    • 1
  • Mila Tomović
    • 3
  • Branislav Šojić
    • 1
  • Snežana Škaljac
    • 1
  • Dragan Vujadinović
    • 4
  • Maja Ivić
    • 1
  • Ilija Djekić
    • 5
  • Igor Tomašević
    • 5
  1. 1.Faculty of Technology Novi SadUniversity of Novi SadNovi SadSerbia
  2. 2.Institute of General and Physical ChemistryUniversity of BelgradeBelgradeSerbia
  3. 3.Technical School Pavle SavićNovi SadSerbia
  4. 4.Faculty of Technology ZvornikUniversity of East SarajevoZvornikBosnia and Herzegovina
  5. 5.Faculty of AgricultureUniversity of BelgradeBelgradeSerbia

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