White striping degree assessment using computer vision system and consumer acceptance test

  • Kato, Talita (Department of Food Science and Technology, State University of Londrina (UEL), Campus Universitario) ;
  • Mastelini, Saulo Martiello (Department of Computer Science, State University of Londrina (UEL), Campus Universitario) ;
  • Campos, Gabriel Fillipe Centini (Department of Computer Science, State University of Londrina (UEL), Campus Universitario) ;
  • Barbon, Ana Paula Ayub da Costa (Department of Animal Science, State University of Londrina (UEL), Campus Universitario) ;
  • Prudencio, Sandra Helena (Department of Food Science and Technology, State University of Londrina (UEL), Campus Universitario) ;
  • Shimokomaki, Massami (Department of Animal Science, State University of Londrina (UEL), Campus Universitario) ;
  • Soares, Adriana Lourenco (Department of Food Science and Technology, State University of Londrina (UEL), Campus Universitario) ;
  • Barbon, Sylvio Jr. (Department of Computer Science, State University of Londrina (UEL), Campus Universitario)
  • Received : 2018.07.02
  • Accepted : 2018.11.23
  • Published : 2019.07.01


Objective: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.


  1. Barbin DF, Mastelini SM, Barbon S, Campos GF, Barbon APA, Shimokomaki M. Digital image analyses as an alternative tool for chicken quality assessment. Biosyst Eng 2016;144:85-93.
  2. O'sullivan MG, Byrne DV, Martens H, Gidskehaug LH, Andersen HJ, Martens M. Evaluation of pork colour: prediction of visual sensory quality of meat from instrumental and computer vision methods of colour analysis. Meat Sci 2003;65:909-18.
  3. Sun X, Chen KJ, Maddock-Carlin KR et al. Predicting beef tenderness using color and multispectral image texture features. Meat Sci 2012;92:386-93.
  4. Kamruzzaman M, Sun D-W, ElMasry G, Allen P. Fast detection and visualization of minced lamb meat adulteration using nir hyperspectral imaging and multivariate image analysis. Talanta 2013;103:130-6.
  5. Argyri AA, Jarvis RM, Wedge D, et al. A comparison of raman and ft-ir spectroscopy for the prediction of meat spoilage. Food Control 2013;29:461-70.
  6. Barbon APAC, Barbon S, Mantovani RG, Fuzyi EM, Peres LM, Bridi AM. Storage time prediction of pork by computational intelligence. Comput Electron Agric 2016;127:368-75.
  7. Qiao J, Wang N, Ngadi M, et al. Prediction of drip-loss, ph, and color for pork using a hyperspectral imaging technique. Meat Sci 2007;76:1-8.
  8. Liu M, Wang M, Wang J, Li D. Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: application to the recognition of orange beverage and chinese vinegar. Sens Actuators B Chem 2013;177:970-80.
  9. Sahoo PK, Soltani S, Wong AKC. A survey of thresholding techniques. Comput Vis Graph Image Process 1988;41:233-60.
  10. Zuiderveld, K. VIII.5. Contrast limited adaptive histogram equalization. In: Heckbert PS. editor. Graphics gems. San Diego, CA, USA: Academic Press; 1994. pp. 474-85.
  11. Campos GFC, Igawa RA, Seixas JL, Almeida AMG, Guido RC, Barbon S. Supervised approach for indication of contrast enhancement in application of image segmentation. In: MMEDIA 2016: the Eighth International Conferences on Advances in Multimedia; 2016; Lisbon, Portugal. p. 12-8.
  12. Balaji GN, Subashini TS, Chidambaram N. Automatic classification of cardiac views in echocardiogram using histogram and statistical features. Procedia Comput Sci 2015;46:1569-76.
  13. Panetta K, Gao C, Agaian S. No reference color image contrast and quality measures. IEEE Trans Consum Electron 2013;59:643-51.
  14. Kuttappan VA, Lee YS, Erf GF, Meullenet JFC, McKee SR, Owens CM. Consumer acceptance of visual appearance of broiler breast meat with varying degrees of white striping. Poult Sci 2012;91:1240-7.
  15. Kuttappan VA, Brewer VB, Clark FD, et al. Effect of white striping on the histological and meat quality characteristics of broiler fillets. Poult Sci 2009;88(E-Suppl. 1):136-7.
  16. Irie M, Kohira K. Simple spot method of image analysis for evaluation of highly marbled beef. Asian-Australas J Anim Sci 2012;25:592-6.
  17. Du C-J, Sun D-W. Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Sci Technol 2004;15:230-49.
  18. Chowdhury S, Verma B, Stockwell D. A novel texture feature based multiple classifier technique for roadside vegetation classification. Expert Syst Appl 2015;42:5047-55.
  19. Shen H-K, Chen P-H, Chang L-M. Automated steel bridge coating rust defect recognition method based on color and texture feature. Autom Constr 2013;31:338-56.
  20. Marsland S. Machine learning: an algorithmic perspective. 2nd edition. Boca Raton, FL, USA: CRC Press; 2015.
  21. Michalski RS, Carbonell JG, Mitchell TM. Machine learning: An artificial intelligence approach. Berlin, Germany: Springer Science & Business Media; 2013.
  22. Breiman L. Random forests. Machine learning 2001;45:5-32.
  23. Haykin S. Neural network: a compressive foundation. 2nd ed. Englewood Cliffs, NJ, USA: Prentice-Hall; 1999.
  24. Vapnik VN. The nature of statistical learning theory. New York, USA: Springer-Verlag New York; 1995.
  25. Ben-Hur A, Weston J. A user's guide to support vector machines. In: Carugo O, Eisenhaber F, editors. Data mining techniques for the life sciences. Methods in molecular biology (methods and protocols), vol 609. New York, USA: Humana Press; 2010.
  26. Riza LS, Bergmeir C, Herrera F, Benitez JM. frbs: Fuzzy rulebased systems for classification and regression in R. J Stat Softw 2015;65:1-30.
  27. Ishibuchi H, Nakashima T, Murata T. Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Trans Syst Man Cybern B Cybern 1999;29:601-18.
  28. Yang Y, Pedersen JO. A comparative study on feature selection in text categorization. ICML; 1997. pp. 412-20.
  29. Petracci M, Mudalal S, Babini E, Cavani C. Occurrence of White striping under commercial conditions and its impact on breast meat quality in broiler chickens. Poult Sci 2013;92:1670-5.
  30. Sanchez BG, Bowker BC, Zhuang H. Comparison of sensory texture attributes of broiler breast fillets with different degrees of white striping. Poult Sci 2016;10:2472-6.