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

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