DEVELOPMENT OF AN INTELLIGENT SYSTEM TO DETERMINE SOUR CHERRY'S ANTIOXIDANT ACTIVITY AND ANTHOCYANIN CONTENT DURING RIPENING

Anthocyanin content and antioxidant activity are believed to display an array of beneficial actions on human health and well-being. This article presents an innovative time and cost economizing method to determine these important attributes of sour cherry during the ripening stage by combining image processing and artificial neural network techniques. For this purpose, six different stages of ripening were selected based on homogeneous size and color of the cherries. The measuring technique consisted of a charge coupled device camera for image acquisition, fluorescent illuminants, capture card, and MATLAB software for image analysis. DPPH and pH differential methods were used to determine antioxidant activity and anthocyanin content, respectively. Several artificial neural networks were designed, trained, and generalized with a back propagation neural network using trainlm' as a training function. Among these networks, two networks with 12-20-6-1 and 12-15-19-1 architectures had the highest correlation coefficients (R = 0.987 and R = 0.904) and the lowest values of MSE (0.027 and 0.186) for modeling anthocyanin content and antioxidant activity values, respectively, from laboratory results and the machine vision method. It was found that anthocyanin content was constantly increased during ripening stages of sour cherry, but antioxidant activity decreased during the early stages of sour cherry development but increased from the second stage.