Color in images: a machine vision approach to the measurement of CIEL*a*b* coordinates in bovine loins

Electronic machine vision systems bring together a set of technologies and techniques used to capture, process and analyze images to perform a specific task, such as object or measurement pattern recognition. These systems rely on image processing and machine learning algorithms to interpret visual information. Therefore, the objective of this research was the construction of an electronic machine vision system (SVA) for color analysis in bovine (longisimus dorsi) loins based on the CIEL*a*b* color space. The VAS implementation was carried out using the programming language Python 3.9 programming language and the color parameters obtained were compared with those obtained on a Minolta CR-400 colorimeter (CM). Both systems were synchronized to provide the user with information about the color coordinates in the samples of loins stored for 6 days at 4°C. The results obtained showed no significant differences. The results obtained showed no significant differences in the values of the L* parameter, while b* and a* showed significant differences during the storage time of the loins. These results are attributed to the oxidation process of the myoglobin and to factors such as breed, feeding and slaughtering process of the cattle, which affect the color of the samples. The results obtained indicate that VAS could be used for the determination of color during the storage of beef loins in real time, offering a non-invasive and low-cost solution to the actors in the meat chain. Keywords: image analysis, beef, colorimeter, artificial vision system.