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Evaluating the potential of artificial neural network and neuro‐fuzzy techniques for estimating antioxidant activity and anthocyanin content of sweet cherry during ripening by using image processing
Author(s) -
TaghadomiSaberi Saeedeh,
Omid Mahmoud,
EmamDjomeh Zahra,
Ahmadi Hojjat
Publication year - 2013
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.6202
Subject(s) - adaptive neuro fuzzy inference system , anthocyanin , artificial neural network , artificial intelligence , ripening , neuro fuzzy , computer science , biological system , fuzzy logic , mathematics , machine learning , pattern recognition (psychology) , food science , fuzzy control system , chemistry , biology
BACKGROUND This paper presents a versatile way for estimating antioxidant activity and anthocyanin content at different ripening stages of sweet cherry by combining image processing and two artificial intelligence ( AI ) techniques. In comparison with common time‐consuming laboratory methods for determining these important attributes, this new way is economical and much faster. The accuracy of artificial neural network ( ANN ) and adaptive neuro‐fuzzy inference system ( ANFIS ) models was studied to estimate the outputs. Sensitivity analysis and principal component analysis were used with ANN and ANFIS respectively to specify the most effective attributes on outputs . RESULTS Among the designed ANNs , two hidden layer networks with 11‐14‐9‐1 and 11‐6‐20‐1 architectures had the highest correlation coefficients and lowest error values for modeling antioxidant activity ( R = 0.93) and anthocyanin content ( R = 0.98) respectively. ANFIS models with triangular and two‐term Gaussian membership functions gave the best results for antioxidant activity ( R = 0.87) and anthocyanin content ( R = 0.90) respectively . CONCLUSION Comparison of the models showed that ANN outperformed ANFIS for this case. By considering the advantages of the applied system and the accuracy obtained in somewhat similar studies, it can be concluded that both techniques presented here have good potential to be used as estimators of proposed attributes. © 2013 Society of Chemical Industry