The Photocolorimetric Methodology as an Alternative Tool for Chicken Meat Color Assessment -

Main Article Content

Manatsanun Nopparatmaitree
Sirawit Sritulanond
Salankorn Khunkhao
Napat Konchan
Aomsin Srapothong
Soranot Chotnipat
Bhutharit Vittayaphathananurak Raksasiri
Jinda Glinubon
Chanchai Arkaphati
Orawan Chaowalit


This study was to determine if a relationship exists between chicken meat color values from colorimeter and photocolorimetric values from generated by the software Adobe Photoshop. In addition, Comparisons of accuracy from the colorimeter and a technique for image analysis in evaluating the color of chicken meat were investigated in this experiment. The result showed that the lightness values of chicken meat from colorimeter was positively correlated with green values from photocolorimetric method (r = 0.576) (P < 0.01) with the form of the linear regression equation, y = 0.1033(x) + 37.389 (R² = 0.3321) (P <0.0001). Additionally, redness values of chicken meat from colorimeter were negatively correlated with green values from photocolorimetric method (r=-0.647) (P<0.01) with the form of the linear regression equation, y = -0.078x + 10.066 (R² = 0.4193) (P < 0.0001). Moreover, this experiment shows that the color assessment was similar between lightness, redness, chroma and hue angle values both generated by photocolorimetric method with equation and chicken meat color values read by the colorimeter (P > 0.05).


Download data is not yet available.

Article Details

Research Article


Antonelli, A., M. Cocchi, P. Fava, G. Foca, G.C. Franchini, D. Manzini and A. Ulrici. 2004. Automated evaluation of food colour by means of multivariate image analysis coupled to a wavelet-based classification algorithm. Analytica Chimica. 515: 3–13.

Barbin, D.F., Kaminishikawahara, C.M. Soares, A.L. Mizubuti, I.Y. Grespan and M. Shimokomaki. 2015. Prediction of chicken quality attributes by near infrared spectroscopy. Food Chem. 168: 554-560.

Benedetti, L.P.S., V. B. dos Santos, T. A. Silva, E. Benedetti-Filho, V. L.Martinsc and O. Fatibello-Filhoa. 2015. A digital image analysis method for quantification of sulfite in beverages. Anal. Methods. 7: 7568–7573.

Carvalho, R.H., A.L. Soares, M. Grespan, R.S. Spurio, F.A.G. Coro and A. Oba. 2015. The effects of the dark house system on growth, performance and meat quality of broiler chicken. Anim. Sci. J. 86: 189-193.

Chmiel, M., M. Słowiński and K. Dasiewicz. 2011. Lightness of the color measured by computer image analysis as a factor for assessing the quality of pork meat. Meat Sci. 88: 566–570.

Choodum, A., K. Parabun, N. Klawach, N. N. Daeid, P. Kanatharana and W. Wongniramaikul. 2014. Real time quantitative colorimetric test for methamphetamine detection using digital and mobile phone technology. Forensic sci. int. 235: 8-13.

Dvořák, P., J. Doležalová and P. Suchý. 2009. Photocolorimetric determination of yolk colour in relation to selected quality parameters of eggs. J. Sci. Food. Agric. 89: 1886–1889.

Garcíaa, A., M.M. Erenas, E.D. Marinettoa, C.A. Abada, Orbe-Paya, A.J. Palmaa, L.F. Capitán-Vallvey. 2011. Mobile phone platform as portable chemical analyzer. Sensors and Actuators B. 156(2011): 350–359.

Girolami, A., F. Napolitano, D. Faraone and A. Braghieri. 2013. Measurement of meat color using a computer vision system. Meat Sci. 93: 111–118.

Heyer, A., K. Andersson, S. Leufvén, L. Rydhmer and K. Lundström. 2005. The effects of breed cross on performance and meat quality of once-bred gilts in a seasonal outdoor rearing system. Arch. Tierz. 48: 359-371.

Jaturasitha, S. 2012. Meat Technology. (4th ed.). Chiang Mai, Thailand: Mingmuang Press. 367 p.

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

Kissel, C., A. L. Soares, A. Rossa and M. Shimokomaki. 2009. Functional properties of PSE (Pale, Soft, Exudative) broiler meat in the production of mortadella. Braz. Arch. Biol. Technol. 52: 213-217.

Kralik, G. Z. Kralik, M. Grcević and D. Hanzek. 2018. Quality of chicken meat. Chapter 4. Animal husbandry and nutrition. 63–94. doi:

Mancini, R. and M. Hunt. 2005. Current research in meat color. Meat Sci. 71: 100–121.

O'Sullivan, M.G., D.V. Byrne, H. Martens, L.H. Gidskehaug, H.J. Andersen and M. Martens. 2003. Evaluation of pork colour: prediction of visual sensory quality of meat from instrumental and computer vision methods of colour analysis. Meat Sci. 65: 909–918.

Park, B.Y., N.K. Kim, C.S. Lee and I.H. Hwang. 2007. Effect of fiber type on postmortem proteolysis in longissimus muscle of Landrace and Korean native black pigs. Meat Sci. 77: 482-491.

Pathare, P.B., U.L. Oparaand and F.A. Al-Said. 2013. Colour measurement and analysis in fresh and processed foods: A review. Food Bioprocess Tech. 6: 36–60.

Petracci, M., M. Betti, M. Bianchi and C. Cavani. 2004. Color variation and characterization of broiler breast meat during processing in Italy. Poult. Sci. 83: 2086–2092.

Quevedo, R. A., J. M. Aguilera and F. Pedreschi. 2010. Color of Salmon Fillets by Computer Vision 5 and Sensory Panel. Food Bioprocess Technol. 3: 637-643.

R Core Team. R. A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. Austria. 2020; URLhttp:/

SAS. 1998. User’s Guide: V.6.12.SAS Institute Inc., Cary, NC.

Smith, D.P. and J.K. Northcutt. 2009. Pale poultry muscle syndrome. Poult. Sci. 88: 1493-1496.

Somnam, S. and M. Kanna. 2019. The Application of Mobile Phone as the Chemical Detector for the Determination of Acidity in Coffee with Flow-based Titrimetric Analysis. Department of Chemistry, Faculty of Science and Technology, Chaingmai Rajabhat University. 50 p. (In Thai)

Sowcharoensuk, C. 2020. Industry Outlook 2020-2022: Chilled, Frozen and Processed Chicken. Krungsri Research 1-10. (In Thai)

Steel, R.G.D. and J.H. Torrie. 1992. Principles and Procedure Statistics. 2nd Edn. McGraw-Hill Book Co., Inc., Singapore.

Sun, X., J. Young, J.H. Liu, L. Bachmeier, R.M. Somers, K.J. Chen, and D. Newman. 2016. Prediction of pork color attributes using computer vision system. Meat Sci. 113: 62–64.

Tapp, W.N., J.W.S. Yancey and J.K. Apple. 2011. How is the instrumental color of meat measured. Meat Sci. 89L–5.

Tomovic, V.M., B.A. Zlender, M.R. Jokanovic, M.S. Tomovic, B.V. Sojic, S.B. Skaljac, T.A. Tasic, P.M. Ikonic, M.M. Soso and N.M. Hromis. 2014. Technological quality and composition of the M. semimembranosus and M. longissimus dorsi from Large White and Landrace Pigs. J. Agric. Food Sci. 23: 9-18.

Trinderup, C.H. and Y.H.B. Kim. 2015. Fresh meat color evaluation using a structured light imaging system. Food Res. Int. 71: 100–107.

Tuntivisoottikul, K. and C. Chaosap. 2005. Meat quality of Thai native pigs, wild pigs and European crossbreds. Proceedings of 44th Kasetsart University Annual Conference: Animal, Veterinary Medicine, Bangkok. 407-415 pp. (In Thai)

Zhang, L., and S. Barbut. 2005. Effects of regular and modified starches on cooked PSE, normal and DFD chicken breast meat batters. Poult Sci. 84: 789-796.