Precise phenotyping method using image data for carcass marbling score in Hanwoo cattle.

With the development of the Korean economy, demand for high-quality beef, specifically Hanwoo beef, is escalating, with marbling traits-measured by the widely used marbling score-being a key contributor to meat palatability. The differences between the high-quality and the lower-quality meat, according to the satisfaction of the customers, are not the result from only the degree of marbling but also from the delicacy of the marbling flecks distribution. Using the computer marbling analysis technique, an index for quantifying marbling fineness of 256 sirloin cuts at 12th- 13th thoracic vertebra named F7 index was developed in this study.

Economic Analysis of the Use of VCS2000 for Pork Carcass Meat Yield Grading in Korea.

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.

Erratum to: Nondestructive Estimation of Lean Meat Yield of South Korean Pig Carcasses Using Machine Vision Technique.

[This corrects the article DOI: 10.5851/kosfa.2018.

Nondestructive Estimation of Lean Meat Yield of South Korean Pig Carcasses Using Machine Vision Technique.

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.