A research group led by Associate Professor Tetsuhito Suzuki used Multi-Sigma® to build a neural network model and analyze data on marbling in Japanese Black cattle (Wagyu) for their study, “Influence of body measurement values, feeding and blood vitamin-A concentration upon marbling score in Japanese Black cattle (Wagyu).”
In the study, body measurements such as body weight, height, and chest circumference, along with feeding-related parameters and blood vitamin A concentrations, were analyzed using a neural network model. The results demonstrated that Beef Marbling Standard (BMS) scores, which indicate the degree of marbling in beef, can be predicted with high accuracy.
Sensitivity analysis and Monte Carlo simulation were also used to quantitatively assess how reductions in blood vitamin A concentrations during the fattening period affect BMS scores. The model developed in this study showed the potential to predict BMS scores even with a relatively small sample size and is expected to serve as a tool to support livestock management in Wagyu production.
For further details, please refer to the paper below.
Publication Details
Title
Influence of body measurement values, feeding and blood vitamin-A concentration upon marbling score in Japanese Black cattle (Wagyu)
Authors
Tetsuhito Suzuki, Moriyuki Fukushima, Mizuki Shibasaki, Tianqi Gao, Pablo Guarnido-Lopez, Namiko Kohama, Shin-ichi Nagaoka, and Naoshi Kondo
Journal
Livestock Science
Publication Year
2026
DOI
https://www.sciencedirect.com/science/article/pii/S1871141326001447
