Quality Assessment of Beef Using Computer Vision Technology

Md. Faizur Rahman1, Abdullah Iqbal2, Md.Abul Hashem1,*, Akinbode A. Adedeji3
Author Information & Copyright
1Department of Animal Science, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh.
2Department of Food Technology and Rural Industries, Bangladesh Agricultural University, Mymensingh, Mymensingh 2202, Bangladesh.
3Department of Biosystems and Agricultural Engineering, 128 C.E. Barnhart Building, University of Kentucky, Lexington KY. 40546 USA
*Corresponding Author: Md. Abul Hashem, Department of Animal Science, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh. Phone: +880-091-67401-6/2633. E-mail:

© Copyright 2020 Korean Society for Food Science of Animal Resources. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: May 13, 2020 ; Revised: Jul 08, 2020 ; Accepted: Jul 22, 2020

Published Online: Jul 23, 2020


Imaging technique or computer vision technology has received huge attention as a rapid and non-destructive technique throughout the world for measuring quality attributes of agricultural products including meat and meat products. This study was conducted to test the ability of computer vision technology to predict the quality attributes of beef. Images were captured from longissimus dorsi muscle in beef at 24 hours post-mortem. Traits evaluated were color value (L*,a*,b*), pH, drip loss, cooking loss, dry matter, moisture, crude protein, ether extract, ash, Thiobarbituric acid reactive substance (TBARS), Peroxide value (POV), Free fatty acid (FFA), Total coliform count (TCC), Total viable count (TVC) and Total yeast-mould count (TYMC). Images were analyzed using the Matlab software (R2015a). Different reference values were determined by physicochemical, proximate, biochemical and microbiological test. All determination was done in triplicate and the mean value was reported. Data analysis was carried out using the programme Stat graphics Centurion XV.I. Calibration and validation model were fitted using the software Unscrambler X version 9.7. A higher correlation found in a* (r = 0.65) and moisture (r = 0.56) with ‘a*’ value obtained from image analysis and the highest calibration and prediction accuracy was found in Lightness (R²c = 0.73, R²p = 0.69) in beef. Results of this work show that computer vision technology may be a useful tool for predicting meat quality traits in the laboratory and meat processing industries.

Keywords: beef quality; computer vision technology; correlation; calibration; validation

Journal Title Change

We announce that the title of our journal and related information were changed as below from January, 2019.


Before (~2018.12)

After (2019.01~)

Journal Title

Korean Journal for Food Science of Animal Resources

Food Science of Animal Resources

Journal Abbreviation

Korean J. Food Sci. An.

Food Sci. Anim. Resour.







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