Analysis of beef quality according to color changes using computer vision and white-box machine learning techniques

Abstract

The quality of beef products relies on the presence of a cherry red color, as any deviation toward brownish tones indicates a loss in quality. Existing studies typically analyze individual color channels separately, establishing acceptable ranges. In contrast, our proposed approach involves conducting a multivariate analysis of beef color changes using white-box machine learning techniques. Our proposal encompasses three phases. (1) We employed a Computer Vision System (CVS) to capture the color of beef pieces, implementing a color correction pre-processing step within a specially designed cabin. (2) We examined the differences among three color spaces (RGB, HSV, and CIELab*) (3) We evaluated the performance of three white-box classifiers (decision tree, logistic regression, and multivariate normal distributions) for predicting color in both fresh and non-fresh beef. These models demonstrated high accuracy and enabled a comprehensive understanding of the prediction process. Our results affirm that conducting a multivariate analysis yields superior beef color prediction outcomes compared to the conventional practice of analyzing each channel independently.

Keywords: Beef color; Computer vision system; Meat quality; White-box machine learning.

Fig. 1

Beef samples. a) and d) Inside skirt. b) and e) Knuckle. c) and f) Sirloin. a), b), and c) Images corresponding to the first day. d), e), and f) Images corresponding to the fifth day. The circles represent the median color of each beef sample. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2

Computer Vision System technologies. a) Cabin. b) Color checker matrix. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3

Image segmentation. a) Original image. b) Binary classifier. c) Connected components. d) Binary fill holes.

Fig. 4

Beef color values in the three color spaces: RGB, HSV, and CIELab*. a) Analysis of R and G channels. b) Analysis of R and B channels. c) Analysis of G and B channels. d) Analysis of H and S channels. e) Analysis of H and V channels. f) Analysis of S and V channels. g) Analysis of L* and a* channels. h) Analysis of L* and b* channels. i) Analysis of a* and b* channels. Green and blue points represent the beef m colors on the first (d1) and the fifth (d5) day after purchase, respectively. Triangles, squares, and circles were used to represent the inside skirt (is), knuckle (k), and sirloin (s) samples. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5

Principal component analysis. Each one of the maps represents the meat samples using different color spaces a) Three color spaces: RGB, HSV, and CIELab*. b) RGB. c) HSV. d) CIELab*. Green and blue points represent the beef colors on the first (d1) and the fifth (d5) day after purchase, respectively. Triangles, squares, and circles were used to represent the inside skirt (is), knuckle (k), and sirloin (s) samples. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6

White-box Machine Learning models for beef quality prediction based on color. a) Logistic Regression classifier. b) Multivariate Normal Distribution classifier. c) Decision Tree classifier. d) Space divisions of the Decision Tree classifier. Green and blue points represent the beef colors on the first and the fifth day after purchase, respectively. Triangles, squares, and circles were used to represent the inside skirt, knuckle, and sirloin samples. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)