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Nondestructive Estimation of Lean Meat Yield of South Korean Pig Carcasses Using Machine Vision Technique
Korean J. Food Sci. An. 2018;38:1109-1119
Published online October 31, 2018;
© 2018 Korean Society for Food Science of Animal Resources

Santosh Lohumi1, Collins Wakholi1, Jong Ho Baek2, Byeoung Do Kim2, Se Joo Kang2, Hak Sung Kim2, Yeong Kwon Yun2, Wang Yeol Lee2, Sung Ho Yoon2, and Byoung-Kwan Cho1,*

1Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea
2Korea Institute for Animal Products Quality Evaluation, Sejong 30100, Korea
Correspondence to: Byoung-Kwan Cho
Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea
Tel: +82-42-821-6715
Fax: +82-42-823-6246
Received September 10, 2018; Revised October 3, 2018; Accepted October 4, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
In this paper, we report the development of a nondestructive prediction model for lean meat percentage (LMP) in Korean pig carcasses and in the major cuts using a machine vision technique. A popular vision system in the meat industry, the VCS2000 was installed in a modern Korean slaughterhouse, and the images of half carcasses were captured using three cameras from 175 selected pork carcasses (86 castrated males and 89 females). The imaged carcasses were divided into calibration (n=135) and validation (n=39) sets and a multilinear regression (MLR) analysis was utilized to develop theprediction equation from the calibration set. The efficiency of the prediction equation was then evaluated by an independent validation set. We found that the prediction equation—developed to estimate LMP in whole carcasses based on six variables—was characterized by a coefficient of determination (Rv2 value of 0.77 (root-mean square error [RMSEV] of 2.12%). In addition, the predicted LMP values for the major cuts: ham, belly, and shoulder exhibited Rv2 values≥0.8 (0.73 for loin parts) with low RMSEV values. However, lower accuracy (Rv2=0.67) was achieved for tenderloin cuts. These results indicate that the LMP in Korean pig carcasses and major cuts can be predicted successfully using the VCS2000-based prediction equation developed here. The ultimate advantages of this technique are compatibility and speed, as the VCS2000 imaging system can be installed in any slaughterhouse with minor modifications to facilitate the on-line and real-time prediction of LMP in pig carcasses.
Keywords : pork grading, lean meat percentage, quality measurement, VCS2000, automation

October 2018, 38 (5)