Development and application of a computer vision system for the measurement of the colour of Iranian sweet bread

Main Article Content

M. Hashemi Shahraki
M. Mashkour
A. Daraei Garmakhany

Keywords

colour measurement, computer vision system, honey powder, image J, Iranian sweet bread

Abstract

A combination of digital camera, computer and graphics software can provide a less expensive and more versatile technique to determine the surface colour of foods. The aim of this work is the development and application of a computer vision system for the measurement of the colour of Iranian sweet bread. In this study, imaging from samples was performed in an environment with 6,500 K illumination standard, and RGB colour space of captured images were transferred to L*a*b* indexes by computer software under illumination standard. This digital image processing was developed as a simple and efficient method for evaluating Iranian sweet bread colour. Also the effect of adding honey on qualitative and quantitative changes of colour values was evaluated as a function of the honey percentage (0, 2, 4, 6, 8, 10, 12, 14% honey powder). Results showed that the colour of the bread produced was improved by adding honey and the developed image processing system was suitable for the measurement of colour parameters. The developed image processing system can be used to measure the surface colour of food.

Abstract 125 | PDF Downloads 126

References

Abdullah, M.Z., Guan, L.C., Lim, K.C. and Karim, A.A., 2004. The applications of computer vision and tomographic radar imaging for assessing physical properties of food. Journal of Food Engineering 61: 125-135.
Adobe Systems, 2002. Adobe PhotoShop 7.0 user guide. Adobe Systems Inc. San Jose, CA, USA.
Brosnan, T. and Sun, D.-W., 2004. Improving quality inspection of food products by computer vision – a review. Journal of Food Engineering 61: 3-16.
Du, C. and Sun, D.-W., 2004. Recent developments in the applications of image processing techniques for food quality evaluation. Trends in Food Science and Technology 15: 230-249.
Forsyth, D. and Ponce, J., 2003. Computer vision: a modern approach. Prentice Hall, Upper Saddle River, NJ, USA.
Francis, F.J. and Clydesdale, F.M., 1975. Food colourimetry: theory and applications. AVI Publishing, Westport, CT, USA.
Gerrard, D.E., Gao, X. and Tan, J., 1996. Beef marbling and colour score determination by image processing. Journal of Food Science61: 145-148.
Gunasekaram, S. and Ding, K., 1994. Using computer vision for food quality evaluation. Food Technology 48: 151-154.
Hashemi Shahraki, M., Maghsoudlou, Y. and Mashkour, M., 2013. Optimisation of humidity absorbers in active packaging of button mushroom by response surface methodology and genetic algorithms. Quality Assurance and Safety of Crops and Foods 5: 227-235.
Hatcher, D.W., Symons, S.J. and Manivannan, U., 2004. Developments in the use of image analysis for the assessment of oriental noodle appearance and colour. Journal of Food Engineering 61: 109-117.
Ilie, A. and Welch, G., 2005. Ensuring colour consistency across multiple cameras. Proceedings of the tenth IEEE international conference on computer vision (ICCV-05). ICCV, pp. 1268-1275.
Lawless, H.T. and Heymann, H., 1998. Sensory evaluation of food: principles and practices. Chapman & Hall, New York, NY, USA.
Leemans, V., Magein, H. and Destain, M.F., 1998. Defects segmentation on ‘Golden Delicious’ apples by using colour machine vision. Computer Electronic Agronomy 20: 117-130.
Luzuriaga, D., Balaban, M.O. and Yeralan, S., 1997. Analysis of visual quality attributes of white shrimp by machine vision. Journal of Food Science 61: 113-118.
Mendoza, F. and Aguilera, J.M., 2004. Application of image analysis for classification of ripening bananas. Journal of Food Science 69: 471-477.
Michalska, A., Amigo-Benavent, M., Zielinski, H. and Del Castillo, M.D., 2008. Effect of bread making on formation of Maillard reaction products contributing to the overall antioxidant activity of rye bread. Journal of Cereal Science48: 123-132.
Ouchemoukh, S., Louaileche, H. and Schweitzer, P., 2007. Physicochemical characteristics and pollen spectrum of some Algerian honeys. Food Control 18: 52-58.
Papadakis, S.E., Abdul-Malek, S., Kamdem, R.E. and Yam, K.L., 2000. A versatile and inexpensive technique for measuring colour of foods. Food Technology 5: 48-51.
Paschos, G., 2001. Perceptually uniform colour spaces for colour texture analysis: an empirical evaluation. IEEE Transactions on Image Processing 10: 932-937.
Pedreschi, F., Aguilera, J.M. and Brown, C.A., 2000. Characterization of food surfaces using scale-sensitive fractal analysis. Journal of Food Process Engineering 23: 127-143.
Pedreschi, F., Mery, D., Mendoza, F. and Aguilera, J.M., 2004. Classification of potato chips using pattern recognition. Journal of Food Science 69: 264-270.
Purlis, E. and Salvadori, V.O., 2009. Modelling the browning of bread during baking. Food Research International 42: 865-870.
Ram, A.K., 2011. Production of spray-dried honey powder and its application in bread. MSc thesis, Louisiana State University, Baton Rouge, LA, USA.
Scanlon, M.G., Roller, R., Mazza, G. and Pritchard, M.K., 1994. Computerized video image analysis to quantify colour of potato chips. American Potato Journal 71: 717-733.
Segnini, S., Dejmek, P. and Öste, R., 1999. A low cost video technique for colour measurement of potato chips. Lebensmittel-Wissenschaft und Technologie 32: 216-222.
Shanin, M.A. and Symons, S.J., 2001. A machine vision system for grading lentils. Canadian Biosystem Engineering 43: 7.7-7.14.
Sun, D.W., 2000. Inspecting pizza topping percentage and distribution by a computer vision method. Journal of Food Engineering 44: 245-249.
Tong, Q., Zhang, X., Wu, F., Tong, J., Zhang, P. and Zhang, J., 2010. Effect of honey powder on dough rheology and bread quality. Food Research International 43: 2284-2288.
Yam, K.L. and Papadakis, S.E., 2004. A simple digital imaging method for measuring and analyzing color of food surfaces. Journal of Food Engineering 61: 137-142.