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 the prediction 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
(Rv(2)=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.
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Currently, the pork industry is incorporating in-line automation with the aim of increasing the slaughtered pork carcass throughput while monitoring quality and safety. In Korea, 21 parameters (such as back-fat thickness and carcass weight) are used for quality grading of pork carcasses. Recently, the VCS2000 system—an automatic meat yield grading machine system—was introduced to enhance grading efficiency and therefore increase pork carcass production. The VCS2000 system is able to predict pork carcass yield based on image analysis. This study also conducted an economic analysis of the system using a cost—benefit analysis. The subsection items of the cost-benefit analysis considered were net present value (NPV), internal rate of return (IRR), and benefit/cost ratio (BC ratio), and each method was verified through sensitivity analysis. For our analysis, the benefits were grouped into three categories: the benefits of reducing labor costs, the benefits of improving meat yield production, and the benefits of reducing pig feed consumption through optimization. The cost-benefit analysis of the system resulted in an NPV of approximately 615.6 million Korean won, an IRR of 13.52%, and a B/C ratio of 1.65.
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