Estimation of sweet cherry antioxidant activity and anthocyanin content during ripening by artificial neural network–assisted image processing technique

This paper presents a new approach for estimating antioxidant activity and anthocyanin content at ripening stages of sweet cherry by combining image processing and artificial neural network (ANN) techniques. The system was consisted of a CCD camera, fluorescent lights, capture card and MATLAB software. Anthocyanin content and antioxidant activity were determined by pH differential and 2, 2‐diphenyl‐1‐picrylhydrazyl methods, respectively. It was found that anthocyanin content was constantly increased during ripening stages, and antioxidant activity decreased during the early stages of development but increased from stage five. Several ANN models were designed and tested. Among these networks, a two hidden layer network with 11‐6‐20‐1 architecture had the highest correlation coefficient (R = 0.965) and the lowest value of mean square error (MSE) (215.4) for modelling anthocyanin content. Similarly, a two hidden layer network with 11‐14‐9‐1 architecture had the highest correlation coefficient (R = 0.914) and the lowest value of MSE (0.070) for modelling antioxidant activity.