Asian-Australasian Journal of Animal Sciences

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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Application of AutoFom III equipment for prediction of primal and commercial cut weight of Korean pig carcasses

  • Choi, Jung Seok (Swine Science and Technology Center, Gyeongnam National University of Science and Technology) ;
  • Kwon, Ki Mun (Korea Institute for Animal Products Quality Evaluation) ;
  • Lee, Young Kyu (Dodram Pig Farmers Cooperative) ;
  • Joeng, Jang Uk (Dodram Pig Farmers Service Co., Ltd.) ;
  • Lee, Kyung Ok (Dodram LPC Co., Ltd.) ;
  • Jin, Sang Keun (Department of Animal Resources Technology, Gyeongnam National University of Science and Technology) ;
  • Choi, Yang Il (Department of Animal Science, Chungbuk National University) ;
  • Lee, Jae Joon (Department of Food and Nutrition, Chosun University)
  • Received : 2018.03.22
  • Accepted : 2018.06.05
  • Published : 2018.10.01
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Abstract

Objective: This study was conducted to enable on-line prediction of primal and commercial cut weights in Korean slaughter pigs by AutoFom III, which non-invasively scans pig carcasses early after slaughter using ultrasonic sensors. Methods: A total of 162 Landrace, Yorkshire, and Duroc (LYD) pigs and 154 LYD pigs representing the yearly Korean slaughter distribution were included in the calibration and validation dataset, respectively. Partial least squares (PLS) models were developed for prediction of the weight of deboned shoulder blade, shoulder picnic, belly, loin, and ham. In addition, AutoFom III's ability to predict the weight of the commercial cuts of spare rib, jowl, false lean, back rib, diaphragm, and tenderloin was investigated. Each cut was manually prepared by local butchers and then recorded. Results: The cross-validated prediction accuracy ($R^2cv$) of the calibration models for deboned shoulder blade, shoulder picnic, loin, belly, and ham ranged from 0.77 to 0.86. The $R^2cv$ for tenderloin, spare rib, diaphragm, false lean, jowl, and back rib ranged from 0.34 to 0.62. Because the $R^2cv$ of the latter commercial cuts were less than 0.65, AutoFom III was less accurate for the prediction of those cuts. The root mean squares error of cross validation calibration (RMSECV) model was comparable to the root mean squares error of prediction (RMSEP), although the RMSECV was numerically higher than RMSEP for the deboned shoulder blade and belly. Conclusion: AutoFom III predicts the weight of deboned shoulder blade, shoulder picnic, loin, belly, and ham with high accuracy, and is a suitable process analytical tool for sorting pork primals in Korea. However, AutoFom III's prediction of smaller commercial Korean cuts is less accurate, which may be attributed to the lack of anatomical reference points and the lack of a good correlation between the scanned area of the carcass and those traits.

Keywords

References

  1. Ministry of Agriculture Food and Rural Affairs. Key statistics of agriculture, livestock and food. Sejong, Korea: Ministry of Agriculture, Food and Rural Affairs; 2015. Report No.: 11-1543000-000128-10.
  2. Korea Institute for Animal Product Quality Evaluation. 2016 Animal products grading statistical yearbook. Sejong, Korea: Korea Institute for Animal Product Quality Evaluation; 2016. Report No.: 11-B552679-000006-10.
  3. Oh SH, See MT. Pork preference for consumers in China, Japan and South Korea. Asian-Australas J Anim Sci 2012;25:143-50.
  4. Font-i-Furnols M, Guerrero L. Consumer preference, behavior and perception about meat and meat products: an overview. Meat Sci 2014;98:361-71. https://doi.org/10.1016/j.meatsci.2014.06.025
  5. Frank D, Oytam Y, Hughes J. Chapter 27-sensory perceptions and new consumer attitudes to meat. In: Purslow, Peter P, editor. New aspects of meat quality. Sawston, England: Woodhead Publishing; 2017. pp. 667-98.
  6. Scholz A, Bunger L, Kongsro J, Baulain U, Mitchell A. Noninvasive methods for the determination of body and carcass composition in livestock: dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging and ultrasound: invited review. Animal 2015;9:1250-64. https://doi.org/10.1017/S1751731115000336
  7. Font i Furnols M, Teran MF, Gispert M. Estimation of lean meat content in pig carcasses using X-ray computed tomography and PLS regression. Chemometr Intell Lab Syst 2009;98:31-7. https://doi.org/10.1016/j.chemolab.2009.04.009
  8. Fortin A, Tong A, Robertson W, et al. A novel approach to grading pork carcasses: computer vision and ultrasound. Meat Sci 2003;63:451-62. https://doi.org/10.1016/S0309-1740(02)00104-3
  9. Font i Furnols M, Gispert M. Comparison of different devices for predicting the lean meat percentage of pig carcasses. Meat sci 2009;83:443-6. https://doi.org/10.1016/j.meatsci.2009.06.018
  10. Pathak V, Singh V, Sanjay Y. Ultrasound as a modern tool for carcass evaluation and meat processing: a review. Int J Meat Sci 2011;1:83-92. https://doi.org/10.3923/ijmeat.2011.83.92
  11. Uttaro B, Zawadski S. Prediction of pork belly fatness from the intact primal cut. Food Control 2010;21:1394-401. https://doi.org/10.1016/j.foodcont.2010.03.012
  12. Brondum J, Egebo M, Agerskov C, Busk H. On-line pork carcass grading with the Autofom ultrasound system. J Anim Sci 1998;76:1859-68. https://doi.org/10.2527/1998.7671859x
  13. Busk H, Olsen E, Brondum J. Determination of lean meat in pig carcasses with the Autofom classification system. Meat Sci 1999;52:307-14. https://doi.org/10.1016/S0309-1740(99)00007-8
  14. Fortin A, Tong AKW, Robertson WM. Evaluation of three ultrasound instruments, CVT-2, UltraFom 300 and AutoFom for predicting salable meat yield and weight of lean in the primals of pork carcasses. Meat Sci 2004;68:537-49. https://doi.org/10.1016/j.meatsci.2004.05.006
  15. Strzelecki J, Komender P, Borzuta K, Lisiak D. Precision of the lean meat content estimation with the automatic grading equipment Autofom on polish pig carcasses. In: Proceedings of the 44th international congress of meat science and technology; 1998. p. 950-1.
  16. Gispert M, Font i Furnols M, Batalle J, Diestre A. The AUTO FOM: new equipment of carcasses clasification approved for Spain. Eurocarne 2002:69-74.
  17. Lisiak D, Duzinski K, Janiszewski P, Borzuta K, Knecht D. A new simple method for estimating the pork carcass mass of primal cuts and lean meat content of the carcass. Anim Prod Sci 2015;55:1044-50. https://doi.org/10.1071/AN13534
  18. Otto G, Roehe R, Looft H, Thoelking L, Kalm E. Comparison of different methods for determination of drip loss and their relationships to meat quality and carcass characteristics in pigs. Meat Sci 2004;68:401-9. https://doi.org/10.1016/j.meatsci.2004.04.007
  19. Nissen PM, Busk H, Oksama M, et al. The estimated accuracy of the EU reference dissection method for pig carcass classification. Meat Sci 2006;73:22-8. https://doi.org/10.1016/j.meatsci.2005.10.009

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