Prediction of pork color attributes using computer vision system. (March 2016)

• Record Type: Journal Article
• Authors: Sun, Xin; Young, Jennifer; Liu, Jeng Hung; Bachmeier, Laura; Somers, Rose Marie; Chen, Kun Jie; Newman, David
• Is Part Of: Meat science. Volume 113(2016:Mar.)
• Page Start: 62
• Page End: 64
• Publication Date: 2016-03
• Subjects: Color feature -- Image processing -- Pork color -- Regression model; Meat -- Periodicals; Meat industry and trade -- Periodicals; Viande -- Périodiques; Viande -- Industrie -- Périodiques; Meat; Meat industry and trade; Periodicals; 641.36
• DOI: 10.1016/j.meatsci.2015.11.009
• Languages: English
• ISSNs: 0309-1740

Abstract

Abstract: Color image processing and regression methods were utilized to evaluate color score of pork center cut loin samples. One hundred loin samples of subjective color scores 1 to 5 (NPB, 2011; n = 20 for each color score) were selected to determine correlation values between Minolta colorimeter measurements and image processing features. Eighteen image color features were extracted from three different RGB (red, green, blue) model, HSI (hue, saturation, intensity) and L*a*b* color spaces. When comparing Minolta colorimeter values with those obtained from image processing, correlations were significant (P < 0.0001) for L* (0.91), a* (0.80), and b* (0.66). Two comparable regression models (linear and stepwise) were used to evaluate prediction results of pork color attributes. The proposed linear regression model had a coefficient of determination (R 2 ) of 0.83 compared to the stepwise regression results (R 2 = 0.70). These results indicate that computer vision methods have potential to be used as a tool in predicting pork color attributes.