A novel method based on digital images in time domain combined with convolutional neural network (CNN) is proposed for discrimination and analysis of the adulterated mutton. For this, 195 sample images during the constant temperature heating process ...
Deep Learning (DL) has proven to be a successful tool for many image classification problems but has yet to be applied to carcass images. The aim of this study was to train DL models to predict carcass cut yields and compare predictions to more stand...
The aim of this study was to predict carcass tissue composition of goat kids using the decision tree with CHAID algorithm (DT) and artificial neural network (ANN) method in comparison with classical step-wise regression (SWR) analyse. Data were obtai...
Pricing of Hanwoo beef in the Korean market is primarily based on meat quality, and particularly on marbling score. The ability to accurately predict marbling score early in the life of an animal is extremely valuable for producers to meet the requir...
Nowadays people tend to include more meat in their diet thanks to the improvement in standards of living as well as an increase in awareness of meat nutritive values. To ensure public health, therefore, there is a need for a rise in worldwide meat pr...
Different multivariate data analysis methods were investigated and compared to optimize rapid and non-destructive quantitative detection of beef adulteration with spoiled beef based on visible near-infrared hyperspectral imaging. Four multivariate st...
The objective of this project was to develop a computer vision system (CVS) for objective measurement of pork loin under industry speed requirement. Color images of pork loin samples were acquired using a CVS. Subjective color and marbling scores wer...
Color image processing and regression methods were utilized to evaluate color score of pork center cut loin samples. One hundred loin samples of subjective color scores 1 to 5 (NPB, 2011; n=20 for each color score) were selected to determine correlat...