title: 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.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors: Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran.
authors: Saeedeh
authors: S
authors: Taghadomi-Saberi
authors: Mahmoud
authors: M
authors: Omid
authors: Zahra
authors: Z
authors: Emam-Djomeh
authors: Hojjat
authors: H
authors: Ahmadi
mesh_terms: D000872:Anthocyanins / Q000032:analysis*; D000975:Antioxidants / Q000032:analysis*; D005638:Fruit / Q000737:chemistry* / Q000254:growth & development*; D017143:Fuzzy Logic; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D027861:Prunus
chemical_list: D000872:Anthocyanins; D000975:Antioxidants
keywords: ANFIS; ANN; anthocyanin content; antioxidant activity; cherry fruit; image processing