Myoglobin redox form prediction in fresh beef using computer vision systems and artificial intelligence

Abstract

To development a computer vision system (CVS) to determine the myoglobin redox forms on beef surfaces, the reflectance spectra of reference samples of deoxymyoglobin (DMb), oxymyoglobin (OMb), and metmyoglobin (MMb) were recorded, and the surface images captured by a digital camera (CVScam) and a cell phone camera (CVScel) to train the algorithm. Meat color changes during blooming and display storage were also recorded. Higher k-fold accuracy was observed for CVScam (90.98 %) than for CVScel (86.53 %), with significantly correlation with colorimeter for OMb (r = 0.77 and 0.71), DMb (r = 0.84 and 0.71), and MMb (r = 0.87 and 0.88). The CVS MMb was lower and the OMb was higher than colorimeter, but the redox form behaviors were more consistent with the expected chemical changes on the surface. The constructed CVS showed satisfactory performance as a useful and accurate tool for predicting the myoglobin redox forms on the beef surface.