Sensory evaluation of the color of mutton by computer vision system

Document Type : Research Articles

Authors

• Samaneh Tabibian
• Mohammad Mohsenzadeh
• Hamidreza Pourreza
• Mahmoodreza Golzarian

Ferdowsi university of Mashhad

10.22067/veterinary.v9i2.63673

Abstract

Evaluation of meat color by a computer vision system (CVS) is a promising implement to dominate the difficulties when the meat is directly evaluated. In this study, 60 Longissimus dorsi from different carcasses of sheep were provided and cut into samples in 5 mm thickness. Immediately under standard shooting conditions, photographing was carried out by CVS. At the same time, the color of meat was measured with Hunterlab colorimeter. The first photo was taken on samples on a freshly cut surface just arrived at the laboratory and the others on 3rd, 5th,7th, 9th, 11th, and 13th days after slaughtering. Then, seven trained sensory panels were asked to evaluate the color of the photos that were taken during 13 days and graded them in order of preference. In general sensory panel preferred samples with high lightness, a relatively high redness, and yellowness until 7 days after slaughtering.

Keywords

• Sheep
• Meat color
• Sensory evaluation
• Mutton

Main Subjects

• Food hygiene

---

References

1. Jimenez-Colmenero F, Carballo J, Cofrades S. Healthier meat and meat products: their role as functional foods. Meat science. 2001;59(1):5-13.

2. Norman J, Berg E, Heymann H, Lorenzen C. Pork loin color relative to sensory and instrumental tenderness and consumer acceptance. Meat science. 2003;65(2):92733.

3. Fischer K. Drip loss in pork: influencing factors and relation to further meat quality traits. Journal of Animal Breeding and Genetics. 2007;124(s1):12-8.

4. Mancini R, Hunt M. Current research in meat color. Meat science. 2005;71(1):100-21.

5. Mitsumoto M, O’Grady MN, Kerry JP, Buckley DJ. Addition of tea catechins and vitamin C on sensory evaluation, colour and lipid stability during chilled storage in cooked or raw beef and chicken patties. Meat Science. 2005;69(4):773-9.

6. Ramirez R, Cava R. The crossbreeding of different Duroc lines with the Iberian pig affects colour and oxidative stability of meat during storage. Meat science. 2007;77(3):339-47.

7. Pedreschi F, Leon J, Mery D, Moyano P. Development of a computer vision system to measure the color of potato chips. Food Research International. 2006;39(10):1092-8.

8. Girolami A, Napolitano F, Faraone D, Braghieri A. Measurement of meat color using a computer vision system. Meat science. 2013;93(1):111-8.

9. Faustman C, Cassens R. The biochemical basis for discoloration in fresh meat: a review. Journal of Muscle Foods. 1990;1(3):217-43.

10. Hunt M, Acton J, Benedict R, Calkins C, Cornforth D, Jeremiah L, et al., editors. Guidelines for meat color evaluation. 44th Annual Reciprocal Meat Conference; 1991.

11. Huff-Lonergan E, Baas TJ, Malek M, Dekkers JC, Prusa K, Rothschild MF. Correlations among selected pork quality traits. Journal of Animal Science. 2002;80(3):617-27.

12. Perry D, Thompson J, Hwang I, Butchers A, Egan A. Relationship between objective measurements and taste panel assessment of beef quality. Animal Production Science. 2001;41(7):981-9.

13. O’sullivan M, Byrne D, Martens H, Gidskehaug L, Andersen H, Martens M. Evaluation of pork colour: prediction of visual sensory quality of meat from instrumental and computer vision methods of colour analysis. Meat Science. 2003;65(2):909-18.

14. Brosnan T, Sun D-W. Improving quality inspection of food products by computer vision––a review. Journal of Food Engineering. 2004;61(1):3-16.

15. Zheng C, Sun D-W, Zheng L. Recent developments and applications of image features for food quality evaluation and inspection–a review. Trends in Food Science & Technology. 2006;17(12):642-55.

16. Chen K, Sun X, Qin C, Tang X. Color grading of beef fat by using computer vision and support vector machine. Computers and Electronics in Agriculture. 2010;70(1):2732.

17. Sun X, Chen K, Berg E, Newman D, Schwartz C, Keller W, et al. Prediction of troponin-T degradation using color image texture features in 10d aged beef longissimus steaks. Meat science. 2014;96(2):837-42.

18. Hunt M, King A, Barbut S, Clause J, Cornforth D, Hanson D, et al. AMSA meat color measurement guidelines. American Meat Science Association, Champaign, Illinois USA. 2012;61820:1-135.

19. Leon K, Mery D, Pedreschi F, Leon J. Color measurement in L∗ a∗ b∗ units from RGB digital images. Food research international. 2006;39(10):1084-91.

20. Jackman P, Sun D-W, Du C-J, Allen P, Downey G. Prediction of beef eating quality from colour, marbling and wavelet texture features. Meat science. 2008;80(4):1273-81.

21. Larrain R, Schaefer D, Reed J. Use of digital images to estimate CIE color coordinates of beef. Food Research International. 2008;41(4):380-5.

22. Jackman P, Sun D-W, Du C-J, Allen P. Prediction of beef eating qualities from colour, marbling and wavelet surface texture features using homogenous carcass treatment. Pattern Recognition. 2009;42(5):75163.

23. Pena F, Molina A, Aviles C, Juarez M, Horcada A. Marbling in the longissimus thoracis muscle from lean cattle breeds. Computer image analysis of fresh versus stained meat samples. Meat science. 2013;95(3):512-9.

24. Chandraratne M, Samarasinghe S, Kulasiri D, Bickerstaffe R. Prediction of lamb tenderness using image surface texture features. Journal of Food Engineering. 2006;77(3):4929.

25. Chen G, Lv D, Pang Z, Liu Q. Red and processed meat consumption and risk of stroke: a meta-analysis of prospective cohort studies. European journal of clinical nutrition. 2013;67(1):91.

26. Birch J. Efficiency of the Ishihara test for identifying red-green colour deficiency. Ophthalmic and Physiological Optics. 1997;17(5):403-8.