Comparison of artificial neural network and response surface methodology in predicting fungal activity and some characteristics of ozonated wheat during storage.
Alireza ghodsvali
1
(
)
Hamid Bakhshabadi
2
(
Department of food science and Technology,Gonbad kavoos Branch,Islamic Azad University,Gonbad Kavoos,Iran
)
Keywords: Ozone, Storage, Wheat, Modeling,
Abstract :
Since predicting changes in agricultural products during storage is very important, this study was conducted with the aim of predicting changes in fungal activity and some characteristics of ozonated wheat during storage. Therefore, in this research, in order to model the wheat grain ozonation process, three levels of ozone concentration (0 to 50 ppm) and three levels of ozonation time (1 to 5 days) were used, and after applying this process, the wheats were kept for 6 months and the number of fungi, the amount of aflatoxin, the percentage of germination and their Zeleny number were investigated. In order to predict the change process of these characteristics, response surface methodology with a central composite design and artificial neural network was used. The results showed that with the increase of ozone concentration, the number of fungus, the amount of aflatoxin decreased, but the amount of Zelani number increased, and with the increase of ozonation time, the number of fungus decreased, but the Zelani number increased. The process optimization results showed that in order to achieve the highest quality wheat, ozone concentration and ozonation time should be 50 ppm and 5 days, respectively. By examining different feedforward backpropagation networks with 2-6-4 topologies with a correlation coefficient greater than 0.991 and a mean square error less than 0.002 and by using the activation function of hyperbolic sigmoid tangent, Lunberg-Marquart learning model and 1000 learning cycles as The best neural model was identified. On the other hand, by comparing the correlation coefficients of the models obtained from the response surface method and the artificial neural network, it was found that the artificial neural network method is more efficient for predicting the changes of ozonized wheat during storage.
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