Determination of Cherry Color Parameters during Rip ening by Artificial Neural Network Assisted Image Process ing Technique

Abstract

Among the different classes of physical properties of foods, color is considered the most important visual attribute in quality perception. C onsumers tend to associate color with quality due to its good correlation with physical, chemical and sensorial evaluations of food quality. This study used an inexpensive method to predict sweet cherries color parameters by combining image processing and artifi cial neural network (ANN) techniques. The color measuring technique consisted of a CCD camera for image acquisition, MATLAB software for image analysis, an d ANN for modeling. To demonstrate the usefulness of this technique, chang es of cherry color during ripening were studied. After designing, training, and genera lizing several ANNs using Levenberg- Marquardt algorithm, a network with 7-14-11-3 archi tecture showed the best correlation (R 2 = 0.9999) for L*, a* and b* values from Chroma meter and the machine vision sy stem. L* and b* parameters decreased during ripening of cherries a nd a* parameter increased at first and then decreased. Evaluation of L* , a* and b* values showed the possibility of reliable use of this system for determination of ab solute color values of foodstuffs with a much lower cost in comparison with Chroma meter. Keywords: Cherry fruit, Color parameters L , a and b , Modeling.