Application of artificial neural network to modeling the effect of calcium ascorbate on extending button mushroom shelf life

Calcium ascorbate is ascorbic acid buffered salt containing calcium and effective in maintaining the quality and reducing losses of agricultural products during post harvest storage. In this study, artificial neural network modeling was used to predicting the effect of calcium ascorbate on button mushroom shelf life. After treatment of calcium ascorbate at three concentrations (0, 0.4 and 0.8%), mushrooms were kept at 1±0.5°C and 90% relative humidity and then qualitative characteristics were evaluated during 0, 10, 15, 20 and 25 days storage. In order to predicting calcium ascorbate effects on button mushroom shelf life, multi-layer perceptron neural network with 2 input (calcium ascorbate concentration and shelf life time) and 14 outputs (weight loss, firmness, TSS, pH, L*, a*, b*, chroma, Hue angle, ΔE, browning index, vitamin C, total phenol and polyphenol oxidase activity) was used. The results showed that a networks with 8 neuron in a hidden layer and using sigmoid function and levenberg–marquardt optimization technique and 40%-20%-40% data for training/ testing/ validating process can be well predict the effect of calcium ascorbate on button mushroom shelf life with correlation coefficient equal 0.91. Results of sensitivity analysis by optimum neural network (2-8-14), was defined shelf life time as the most effective factor in predicting button mushroom attributes during post harvest storage.