Mr. Yitao Liao Profile

Yitao Liao

at Zhejiang Univ

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Proceedings Article | 20 April 2010 Paper

On-line determination of pork color and intramuscular fat by computer vision

Yi-Tao Liao, Yu-Xia Fan, Xue-Qian Wu, Li-juan Xie, Fang Cheng

Proceedings Volume 7676, 76760W (2010) https://doi.org/10.1117/12.850715

KEYWORDS: Image segmentation, RGB color model, Machine vision, Computer vision technology, Image processing, Particles, Chemical analysis, Calibration, Image quality, Image analysis

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In this study, the application potential of computer vision in on-line determination of CIE L*a*b* and content of intramuscular fat (IMF) of pork was evaluated. Images of pork chop from 211 pig carcasses were captured while samples were on a conveyor belt at the speed of 0.25 m•s-1 to simulate the on-line environment. CIE L*a*b* and IMF content were measured with colorimeter and chemical extractor as reference. The KSW algorithm combined with region selection was employed in eliminating the surrounding fat of longissimus dorsi muscle (MLD). RGB values of the pork were counted and five methods were applied for transforming RGB values to CIE L*a*b* values. The region growing algorithm with multiple seed points was applied to mask out the IMF pixels within the intensity corrected images. The performances of the proposed algorithms were verified by comparing the measured reference values and the quality characteristics obtained by image processing. MLD region of six samples could not be identified using the KSW algorithm. Intensity nonuniformity of pork surface in the image can be eliminated efficiently, and IMF region of three corrected images failed to be extracted. Given considerable variety of color and complexity of the pork surface, CIE L*, a* and b* color of MLD could be predicted with correlation coefficients of 0.84, 0.54 and 0.47 respectively, and IMF content could be determined with a correlation coefficient more than 0.70. The study demonstrated that it is feasible to evaluate CIE L*a*b* values and IMF content on-line using computer vision.

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