Texture analysis of MR image for predicting the firmness of Huanghua pears (Pyrus pyrifolia Nakai, cv. Huanghua) during storage using an artificial neural network.
Firmness, a main index of quality changes, is important for the quality evaluation of fruits. In the present study, texture analysis (TA) of magnetic resonance images was applied to predict the firmness of Huanghua pears (Pyrus pyrifolia Nakai, cv. Huanghua) during storage using an artificial neural network (ANN). Seven co-occurrence matrix-derived TA parameters and one run-length matrix TA parameter significantly correlated with firmness were considered as inputs to the ANN. Several ANN models were evaluated when developing the optimal topology. The optimal ANN model consisted of one hidden layer with 17 neurons in the hidden layer. This model was able to predict the firmness of the pears with a mean absolute error (MAE) of 0.539 N and R=0.969. Our data showed the potential of TA parameters of MR images combined with ANN for investigating the internal quality characteristics of fruits during storage.
Zhou
Ran
R
Institute of Refrigeration and Cryogenic Engineering, Shanghai Jiao Tong University, 201101 Shanghai, PR China.
Li
Yunfei
Y
D005511:Food Handling; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D031989:Pyrus