Register      Login
Animal Production Science Animal Production Science Society
Food, fibre and pharmaceuticals from animals
RESEARCH ARTICLE

A new simple method for estimating the pork carcass mass of primal cuts and lean meat content of the carcass

Dariusz Lisiak A , Kamil Duziński B C , Piotr Janiszewski A , Karol Borzuta A and Damian Knecht B
+ Author Affiliations
- Author Affiliations

A Division of Meat and Fat Technology, Prof. Wacław Dąbrowski Institute of Agricultural and Food Biotechnology, 04-190 Warsaw, ul. Jubilerska 4, Poland.

B Institute of Animal Breeding, Wroclaw University of Environmental and Life Sciences, Chelmonskiego 38C, 51–630 Wroclaw, Poland.

C Corresponding author. Email: kamil.duzinski@up.wroc.pl

Animal Production Science 55(8) 1044-1050 https://doi.org/10.1071/AN13534
Submitted: 31 July 2013  Accepted: 28 May 2014   Published: 15 September 2014

Abstract

The aim of this study was to develop regression equations for estimating lean meat content and the mass of primal cuts (ham, loin, shoulder, belly) based on selected linear measurements. The experiment involved a classification of 141 pigs from the Polish commercial pig population, with hot carcass weight ranging between 60 and 120 kg. The study population was characterised by high variability in terms of analysed measurements. Eight measurements were made including: mass of half-carcass, backfat thickness at different points (over shoulder, over last rib, over the middle of M. gluteus medius), width and thickness of the M. longissimus dorsi measured over the last rib, thickness of the lumbar and the gluteal muscle layer located between the spinal cord and beginning of the M. gluteus medius and waist width – the width of the carcass measured at the narrowest point of the lumbar. A subjective five-point scale was used to score difficulties in obtaining linear measurements (workload rate). The lean meat percentage and mass of cuts were determined by dissection. The study enabled equations to be devised for estimating lean meat content with an accuracy greater than most devices used for carcass classification (estimation error 1.67). Regression coefficients for the mass of primal cuts were: 0.92 for ham, 0.87 for loin, 0.87 for shoulder, and 0.74 for belly. The error of equations used to estimate the mass of primal cuts were: 391 g for ham, 447 g for loin, 263 g for shoulder and 257 g for belly. The workload rate for all the developed regression equations ranged from 1.3 to 1.6 points. The outcome of this study was the development of equations to predict carcass value without the need to use expensive classification equipment.

Additional keywords: meatiness, regression equations.


References

Borzuta K, Lisiak D, Borys A, Strzelecki J, Magda F, Grześkowiak E, Lisiak B (2010) Study on the effect of lean meat content on commercial value of porcine carcass. Nauka Przyroda Technolologie 4, 1–14.

Brondum J, Egebo M, Agerskov C, Busk H (1998) On-line pork carcass grading with the Autofom ultrasound system. Journal of Animal Science 76, 1859–1868.

Buczyński JT, Swulińska-Katulska A, Chojnacka R, Szulc K (2005) Assessment of eating quality of meat from Złotnicka White and Złotnicka Spotted pigs. Annals of Animal Science 1, 7–10.

Busk H, Olsen EV, Brondum J (1999) Determination of lean meat in pig carcasses with the Autofom classification system. Meat Science 52, 307–314.
Determination of lean meat in pig carcasses with the Autofom classification system.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC3MbnsFSlsg%3D%3D&md5=a4d7e2a97b8fee022565083ed97438b3CAS | 22062580PubMed |

Collewet G, Bogner P, Allen P, Busk H, Dobrowolski A, Olsen E, Davenel A (2005) Determination of the lean meat percentage of pig carcasses using magnetic resonance imaging. Meat Science 70, 563–572.
Determination of the lean meat percentage of pig carcasses using magnetic resonance imaging.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC3Mbns1WgsA%3D%3D&md5=7420452c5f479b3b355b1074034a56afCAS | 22063881PubMed |

European Union (2008) Laying down detailed rules on the implementation of the Community scales for the classification of beef, pig and sheep carcasses and the reporting of prices thereof, Commission Regulation (EC) No. 1249/2008 of 10 December 2008, Brussels.

Edwards DB, Bates RO, Osburn WN (2003) Evaluation of Duroc- vs. Pietrain-sired pigs for carcass and meat quality measures. Journal of Animal Science 81, 1895–1899.

Forrest JC, Kuei CH, Orcutt MW, Schinckel AP, Stouffer JR, Judge MD (1989) A review of potential new methods of on-line pork carcass evaluation. Journal of Animal Science 67, 2164–2170.

Johnson RK, Berg EP, Goodwin E, Mabry JW, Miller RK, Robison OW, Sellers H, Tokach MD (2004) Evaluation of procedures to predict fat-free lean in swine carcasses. Journal of Animal Science 82, 2428–2441.

Kosovac O, Vidovic V, Zivkovic B, Radovic C, Smiljakovic T (2009) Quality of pig carcasses on slaughter line according to previous and current EU regulation. Biotechnology in Animal Husbandry 25, 791–801.

Lisiak D, Grześkowiak E, Borys A, Borzuta K, Strzelecki J, Magda F, Lisiak B, Powałowski K (2011) The effect of porcine carcass meatiness on field of meat and fat processing. Nauka Przyroda Technologie 5, 1–13.

Lisiak D, Borzuta K, Janiszewski P, Magda F, Grześkowiak E, Strzelecki J, Powałowski K, Lisiak B (2012) Verification of regression equations for estimating pork carcass meatiness using CGM, IM-03, Fat-O-Meat’er II and UltraFom 300 devices. Annals of Animal Science 12, 585–596.
Verification of regression equations for estimating pork carcass meatiness using CGM, IM-03, Fat-O-Meat’er II and UltraFom 300 devices.Crossref | GoogleScholarGoogle Scholar |

Marcoux M, Pomar C, Faucitano L, Brodeur C (2007) The relationship between different pork carcass lean yield definitions and the market carcass value. Meat Science 75, 94–102.
The relationship between different pork carcass lean yield definitions and the market carcass value.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC3MbnsFOnsA%3D%3D&md5=f7565536a532eaa1f3c4159bbe1bb7ecCAS | 22063416PubMed |

Ministry of Agriculture and Rural Development (2013) Pork Market Bulletin, Integrated Agricultural Market Information No. 21, Warsaw. Available at http://www.minrol.gov.pl [Verified 4 August 2014]

Nissen PM, Busk H, Oksama M, Seynaeve M, Gispert M, Walstra P, Hansson I, Olsen E (2006) The estimated accuracy of the EU reference dissection method for pig carcass classification. Meat Science 73, 22–28.
The estimated accuracy of the EU reference dissection method for pig carcass classification.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC3MbnsVKitg%3D%3D&md5=1772bab142ab354d088d4d73862f7d52CAS | 22062050PubMed |

Olsen EV, Candek-Potokar M, Oksama M, Kien S, Lisiak D, Busk H (2007) On-line measurements in pig carcass classification: repeatability and variation caused by the operator and the copy of instrument. Meat Science 75, 29–38.
On-line measurements in pig carcass classification: repeatability and variation caused by the operator and the copy of instrument.Crossref | GoogleScholarGoogle Scholar | 22063408PubMed |

Romvári R, Dobrowolski A, Repa I, Allen P, Olsen E, Szabó A, Horn P (2006) Development of a computed tomographic calibration method for the determination of lean meat content in pig carcasses. Acta Veterinaria Hungarica 54, 1–10.
Development of a computed tomographic calibration method for the determination of lean meat content in pig carcasses.Crossref | GoogleScholarGoogle Scholar | 16613021PubMed |

Schinckel AP, Herr CT, Richert BT, Forrest JC, Einstein ME (2003) Ractopamine treatment biases in the prediction of pork carcass composition. Journal of Animal Science 81, 16–28.

Skiba G, Raj S, Poławska E, Pastuszewska B, Elminowska-Wenda G, Bogucka J, Knecht D (2012) Profile of fatty acids, muscle structure and shear force of musculus longissimus dorsi (MLD) in growing pigs as affected by energy and protein or protein restriction followed by realimentation. Meat Science 91, 339–346.
Profile of fatty acids, muscle structure and shear force of musculus longissimus dorsi (MLD) in growing pigs as affected by energy and protein or protein restriction followed by realimentation.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XlvFGhs7g%3D&md5=a583745670106262c104589cf3f02366CAS | 22424756PubMed |

Sönnichsen M, Dobrowolski A, Höreth R, Branscheid W (2002) Videobildauswertung an Schweinehalften. Fleischwirtschaft 82, 98–101.

Šprysl M, Čitek J, Stupka R, Vališ L, Vitek M (2007) The accuracy of FOM instrument used in on-line pig carcass classification in the Czech Republic. Czech Journal of Animal Science 52, 149–158.

STATISTICA (2012) ‘Data analysis, software system. Version 10.’ (StatSoft Inc.: Krakow, Poland)

Świtoński M, Stachowiak M, Cieślak J, Bartz M, Grześ M (2010) Genetics of FAT tissue accumulation in pigs: a comparative approach. Journal of Applied Genetics 51, 153–168.
Genetics of FAT tissue accumulation in pigs: a comparative approach.Crossref | GoogleScholarGoogle Scholar | 20453303PubMed |

Szulc K, Skrzypczak E, Buczyński JT, Stanisławski D, Jankowska-Mąkosa A, Knecht D (2012) Evaluation of fattening and slaughter performance and determination of meat quality in Złotnicka Spotted pigs and their crosses with the Duroc breed. Czech Journal of Animal Science 57, 95–107.

Vester-Christensen M, Erbou SGH, Hansen MF, Olsen EV, Christensen LB, Hviid M, Ersboll BK, Larsen R (2009) Virtual dissection of pig carcasses. Meat Science 81, 699–704.
Virtual dissection of pig carcasses.Crossref | GoogleScholarGoogle Scholar | 20416568PubMed |

Vitek M, Pulkrabek J, Valis L, David L, Wolf J (2008) Improvement of accuracy in the estimation of lean meat content in pig carcasses. Czech Journal of Animal Science 53, 204–211.

Wajda S, Winiarski R, Śmiecińska K (2006) Slaughter quality of fatteners slaughtered at different live weights. Annals of Animal Science 2, 439–443.

Walstra P, Merkus GSM (1996) Procedure for assessment of the lean meat percentage as a consequence of the new EU reference dissection method in pig carcass classification. (ID-DLO Research Branch: Zeist, Netherlands)