Color grading of beef fat by using computer vision and support vector machine

Authors:
K. Chen;X. Sun;Ch. Qin;X. Tang
Affiliations:
The College of Engineering, Nanjing Agricultural University, Nanjing 210031, PR China;The College of Engineering, Nanjing Agricultural University, Nanjing 210031, PR China;The College of Engineering, Nanjing Agricultural University, Nanjing 210031, PR China;Institute of Quality Standard & Testing Technology for Agro-Products, CAAS, Beijing 10081, PR China
Venue:
Computers and Electronics in Agriculture
Year:
2010

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Abstract

Machine vision and support vector machine (SVM) were used to determine color scores of beef fat. One hundred and twenty-three of beef rib eye steaks were selected to sensory evaluation and image processing. After fat color score was assigned to each steak by a five-member panel according to the standard color cards, images were acquired for each steak. The subcutaneous fat was separated from the rib eye by using a sequence of image processing algorithms, boundary tracking, thresholding and morphological operation, etc. Twelve features of fat color (six features were extracted from the subcutaneous fat images and the other six were calculated) were used as input for SVM classifiers. The best SVM classifier was chosen according to percentage of correct classified samples based on the training set and then was validated by a nondependent test set. The proposed SVM classifier achieved the best performance percentage of 97.4%, showing that the machine vision combined with SVM discrimination method can provide an effective tool for predicting color scores of beef fat.