Theory and practice of meat processing

Advanced search


Full Text:


Meat and meat products color evaluation ability of a computer vision system (CVS) is investigated by a comparison study with color measurements from a traditional colorimeter. A statistical analysis revealed significant differences between the instrumental values in all three dimensions (L*, a*, b*) between the CVS and colorimeter. The CVS-generated colors were more similar to the sample of the meat products visualized on the monitor, compared to colorimeter-generated colors in all (100 %) individual trials performed. The use of CVS should be considered a superior alternative to the traditional method for measuring color of meat and meat products.

About the Author

Igor B. Tomasevic
University of Belgrade, Belgrade

Associate professor, Department of Animal Source Food Technology, Faculty of Agriculture

11080, Serbia, Belgrade, Nemanjina str., 6
Tel.: +7–38–160–429–99–98


1. Mancini, R. A., Hunt, M. C. (2005). Current research in meat color. Meat Science, 71 (1), 100–121.

2. Font-i-Furnols, M., Guerrero, L. (2014). Consumer preference, behavior and perception about meat and meat products: An over- view. Meat Science, 98(3), 361–371.

3. Wideman, N., O’Bryan, C., Crandall, P. (2016). Factors affecting poultry meat colour and consumer preferences-A review. World’s Poultry Science Journal, 72(2), 353–366.

4. Kang, S., East, A., Trujillo, F. (2008). Colour vision system evaluation of bicolour fruit: A case study with ‘B74’mango. Postharvest Biology and Technology, 49 (1), 77–85.

5. Girolami, A., Napolitano, F., Faraone, D., Braghieri, A. (2013). Measurement of meat color using a computer vision system. Meat Science, 93(1), 111–118.

6. Wang, Q., Wang, H., Xie, L., Zhang, Q. (2012). Outdoor color rating of sweet cherries using computer vision. Computers and Electronics in Agriculture, 87, 113–120.

7. Issac, A., Dutta, M. K., Sarkar, B. (2017). Computer vision based method for quality and freshness check for fish from segmented gills. Computers and Electronics in Agriculture, 139, 10–21.

8. Wan, P., Toudeshki, A., Tan, H., Ehsani, R. (2018). A methodology for fresh tomato maturity detection using computer vision. Computers and Electronics in Agriculture, 146, 43–50.

9. Heinz, G., Hautzinger, P. (2007). Meat processing technology for small-to medium-scale producers (RAP publication 2007/20 ed.). Bangkok: Food and Agriculture Organization of the United Nations (FAO).

10. Tomasevic, I., Tomovic, V., Milovanovic, B., Lorenzo, J. M., Đorđević, V., Karabasil, N., Djekic, I. (2019). Comparison of a computer vision system vs. traditional colorimeter for color evaluation of meat products with various physical properties. Meat Science, 148, 5–12

11. Fernández-Vázquez, R., Stinco, C. M., Hernanz, D., Heredia, F. J., Vicario, I. M. (2013). Colour training and colour differences thresholds in orange juice. Food Quality and Preference,30 (2), 320–327.

12. Mokrzycki, W. S., Tatol, M. (2011). Color difference ΔE — a survey. Machine Graphics and Vision, 20 (4), 383–411.

13. Tomasevic, I., Tomovic, V., Milovanovic, B., Lorenzo, J. M., Pighin, D., Natsasijevic, I., Stajic. S., Djekic, I. (2018). Evaluation of poultry meat colour using computer vision system and colourimeter — is there a difference? British Food Journal — Accepted manuscript.

14. Larraín, R., Schaefer, D., Reed, J. (2008). Use of digital images to estimate CIE color coordinates of beef. Food Research International, 41(4), 380–385.

15. Brainard, D. H. (2003). Color appearance and color difference specification. The science of color, 2, 191–216.

16. Daszkiewicz, T., Kondratowicz, J., Koba-Kowalczyk, M. (2011). Changes in the quality of meat from roe deer (Capreolus capreolus L.) bucks during cold storage under vacuum and modified atmosphere. Polish Journal of Veterinary Sciences, 14(3), 459–466.

17. Volpelli, L. A., Valusso, R., Piasentier, E. (2002). Carcass quality in male fallow deer (Dama dama): effects of age and supplementary feeding. Meat Science, 60(4), 427–432.

18. Tomasevic, I., Tomovic, V., Milovanovic, B., Vasilev, D., Jokanovic, M., Šojić, B., Lorenzo. M, Djekic, I. (2018). How the color of game meat should be measured: computer vision system vs. colorimeter. Fleischwirtschaft — Accepted manuscript.

19. Vargas-Sánchez, R. D., Torrescano-Urrutia, G. R., Ibarra-Arias, F. J., Portillo-Loera, J. J., Ríos-Rincón, F. G., Sánchez-Escalante, A. (2018). Effect of dietary supplementation with Pleurotus ostreatus on growth performance and meat quality of Japanese quail. Livestock Science, 207, 117–125.

20. Valous, N. A., Mendoza, F., Sun, D. — W., Allen, P. (2009). Colour calibration of a laboratory computer vision system for quality evaluation of pre-sliced hams. Meat science,81 (1), 132–141.

21. Ramirez-Navas, J. S., Rodriguez de Stouvenel, A. (2012). Characterization of Colombian quesillo cheese by spectrocolorimetry. Vitae, 19(2), 178–185.

22. Girolami, A., Napolitano, F., Faraone, D., Di Bello, G., Braghieri, A. (2014). Image analysis with the computer vision system and the consumer test in evaluating the appearance of Lucanian dry sausage. Meat science, 96 (1), 610–616.

23. Oleari, C. (1998). Misurare il colore: spettrofotometria, fotometria e colorimetria: fisiologia e percezione: Hoepli.


For citations:

Tomasevic I.B. COMPUTER VISION SYSTEM FOR COLOR MEASUREMENTS OF MEAT AND MEAT PRODUCTS: A REVIEW. Theory and practice of meat processing. 2018;3(4):4-15.

Views: 737

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

ISSN 2414-438X (Print)
ISSN 2414-441X (Online)