TY  - JOUR
T1  - Predicting Fresh Beef Color Grade Using Machine Vision Imaging and Support Vector Machine (SVM) Analysis
AU - Berg, E.P. AU - Magolski, J.D. AU - Chen, K. AU - Sun, X. 
JO  - Journal of Animal and Veterinary Advances
VL  - 10
IS  - 12
SP  - 1504
EP  - 1511
PY  - 2011
DA  - 2001/08/19
SN  - 1680-5593
DO  - javaa.2011.1504.1511
UR  - https://makhillpublications.co/view-article.php?doi=javaa.2011.1504.1511
KW  - China
KW  -SVM
KW  -image analysis
KW  -multiple linear regression
KW  -Beef muscle
KW  -US
AB  - This study investigates the usefulness of electronically derived and analyzed fresh beef lean color image features for predicting official Chinese beef color scores. About 160 beef longissimus thoracis (ribeye) cross-section images were collected. The twelve features of beef muscle color were extracted and one feature was calculated using stepwise multiple regression analysis. Multiple linear regression and SVM model with inputs of color features and outputs of 4-7 color scores, respectively were designed to automatically estimate the grade of beef muscle color. Multiple linear regression analysis of the coefficient of determination (R<SUP>2</SUP> = 0.89) and the model accuracy which determine the beef color muscle scores is 86.8%. SVM classifier achieved the best performance percentage of 94.7% showing that the machine vision combined with SVM discrimination method can provide an effective tool for predicting color scores of beef muscle.
ER  -