MACHINE VISION METHOD FOR QUALITY EVALUATION OF COW MEAT

Document Type : Original Article

Authors

1 Associate Prof., Nat. Inst. of Laser Enhanced Sc. (NILES), Cairo Univ., Egypt.

2 Senior Researcher, Agric. Eng. Res. Institute, Agric. Res. Center, Dokki, Egypt.

3 Postgraduate, Nat. Inst. of Laser Enhanced Sc. (NILES), Cairo Univ., Egypt.

Abstract

The aim of the present research was to determine physical and chemical properties of different muscles cut of cow meat, studying effect of physical and chemical changes on color properties of meat samples and quality evaluation for cow meat using image processing.Measurements of color properties were carried out at the Laboratory of Laser Application in Agriculture Engineering at National Institute of Laser Enhanced Science (NILES), Egypt. While, the physical and chemical properties of meat samples were analyzed in the Research Park, Faculty of Agriculture, Cairo University, Giza, Egypt. The samples, were abscised from four muscles representing common retail cuts in the local market in Egypt (Round (Leg), Shoulder, Hind shank and Best ribs).  Determination of chemical properties of the meat samples(moisture content, protein, fat, ash, pH and collagen) and physical characteristics (cooking loss, water holding capacity, and shear force) and color  properties (intensity, saturation values and hue degree) were measured during season 2008 - 2009.
The obtained results were as following: (a) By increasing of the shear force and the water holding capacity and decreasing the cooking losses of meat sample, these result led to the lowering of intensity and saturation values. While increases of the hue degree for best ribs, shoulder, round and hind shank of meat cut type of cow., (b)By decreasing the moisture content, fat, and ash percentages and increasing the protein, collagen percents and pH values led to decrease the intensity and saturation values of meat cut while hue degree was increased for best ribs, shoulder, round and hind shank, respectively., (c) The best ribs sample has high intensity of light, lighter of saturation and red color degree than shoulder, round and hind shank samples. While, the hind shank sample was the lowest of intensity, darker and more red color degree., and (d) Image processing as a machine vision technique can be used for evaluating quality of meat cut types.   

Main Subjects

References

Abril, M.; M.M. Campo; A. Onenç; C. Sanudo; P. Alberti   and A. I. Negueruela (2001). Beef color evaluation as a function of ultimate pH. Meat Science, 58, 69-78.
American Meat Science Association (1991).Guidelines for Meat Color Evaluation. Published by the American Meat Science Association.  44 : 5-12.
Balkeninues, M.N.; S. S. Kadam and D. Drost  (2003). Evaluation of some food color by image analysis.   Journal of Food Engineering, 54, 233–238.
Chance Brooks (2007).  Product Enhancement Beef Packaging Cattlemen’s Beef Board.  National cattlemen's association, Pages: 1-5.
Chandraratne, M. R.  ; S. Samarasinghe; D. Kulasiri  and R. Bickerstaffe (2006).  Prediction of lamb tenderness using image surface texture features. Journal of Food Engineering, 77, 492-499.
Dave McKenna Meat Science Section (2007).The Color of Meat. Meat Science Section . Department of AnimalScienceTexasA&MUniversity  pages 1-2.
Denoyelle, C. and F. Berny (1999). Objective measurement of veal color for classification purposes. Meat Sci. 53:203–209.
Gunasekaran, S. (2001). Non-destructive food evaluation techniques to analyze properties and quality. Food Sci. and Tech. (vol.105) New York: Marcel Decker.
Hal Good, (2007). Solving Color Measurement Challenges of the Food Industry  Hunter Lab., Reston, VA Pp: 1-4.
Hatem, I.; J. Tan and D. E. Gerrard (2003). Determination of animal skeletal maturity by image processing. Meat Science, 65:999-1004.
James, R. (2007). Color Changes in Cooked Beef .University of Wisconsin-Madison Cattlemen’s Beef Board. Pages: 1-5.
Kayaardi, S. and V. Gök (2003). Effect of replacing beef fat with olive oil on quality characteristics Of  Turkish soudjouk (sucuk). Meat Science, 66:249-257.
Li, J.; j. Tan and p. Shatadal (2001). Classification of tough and tender beef by image texture analysis. Journal of Meat Science, 57, 341–346.
Lu, J.; J. Tan; P. Shatadal and D. E. Gerrard (2000). Evaluation of pork color by using computer vision. Meat Science, 56: 57-60.
Sun, D. W. (2000). Inspecting pizza topping percentage and distribution by a computer vision method. Journal of Food Engineering, 44, 245–249.

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