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. 2022 Jan;64(1):155-165.
doi: 10.5187/jast.2021.e133. Epub 2022 Jan 31.

Relationship between porcine carcass grades and estimated traits based on conventional and non-destructive inspection methods

Seok-Won Lim  1 , Doyon Hwang  2 , Sangwook Kim  1 , Jun-Mo Kim  1
Affiliations

Relationship between porcine carcass grades and estimated traits based on conventional and non-destructive inspection methods

Seok-Won Lim et al. J Anim Sci Technol. 2022 Jan.

Abstract

As pork consumption increases, rapid and accurate determination of porcine carcass grades at abattoirs has become important. Non-destructive, automated inspection methods have improved slaughter efficiency in abattoirs. Furthermore, the development of a calibration equation suitable for non-destructive inspection of domestic pig breeds may lead to rapid determination of pig carcass and more objective pork grading judgement. In order to increase the efficiency of pig slaughter, the correct estimation of the automated-method that can accommodate the existing pig carcass judgement should be made. In this study, the previously developed calibration equation was verified to confirm whether the estimated traits accord with the actual measured traits of pig carcass. A total of 1,069,019 pigs, to which the developed calibration equation, was applied were used in the study and the optimal estimated regression equation for actual measured two traits (backfat thickness and hot carcass weight) was proposed using the estimated traits. The accuracy of backfat thickness and hot carcass weight traits in the estimated regression models through stepwise regression analysis was 0.840 (R 2) and 0.980 (R 2), respectively. By comparing the actually measured traits with the estimated traits, we proposed optimal estimated regression equation for the two measured traits, which we expect will be a cornerstone for the Korean porcine carcass grading system.

Keywords: Backfat thickness; Carcass weight; Meat grading; Non-destructive inspection method; Porcine carcass.

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Conflict of interest statement

No potential conflict of interest relevant to this article was reported.

Figures

Fig. 1.
Fig. 1.. Boxplots showing that differences in measured two traits (backfat thickness and carcass weight) according to each fixed effect.
T-test, ***p < 0.001. A, B, and C represent the effects of abattoir, sex, and season on backfat thickness, respectively. D, E, and F represent the effects of abattoir, sex, and season effects for carcass weight, respectively. The horizontal line in the box represents the median, and the red rhombus indicates the mean.
Fig. 2.
Fig. 2.. Heatmap showing the correlations between the two measured trait (backfat thickness and carcass weight) and the 46 estimated traits.
Colour scale bar from red to blue represents the degree of correlation coefficients. Yellow border indicates estimated traits that exhibiting the highest correlation coefficients with measured backfat thickness trait in all abattoirs. Green border indicates estimated traits that showing the highest correlation coefficients with measured carcass weight trait in all abattoirs. Data source for the plots can be found in Supplementary Table S2.
Fig. 3.
Fig. 3.. Linear regression plots of measured traits (backfat thickness and carcass weight) versus estimated top three traits.
The x-axis represents the estimated traits, whereas the y-axis represents the measured traits (A–C, backfat thickness; D–F, carcass weight). The colours in the linear regression plots represent scatter plots corresponding to four pork grades (1+, yellow; 1, red; 2, green; extra, blue).
See this image and copyright information in PMC

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