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Author: search.filters.author.Toktam Mohammadi-MoghaddamSubject: search.filters.subject.Artificial neural network

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    Comparison of Sigmoid Logarithm and Hyperbolic Tangent Functions in Modeling the Oxidation Parameters of Soybean Oil Containing Extract of Black Plum Peels Natural Antioxidant
    (United Scientific Group, 2024-09-06)
    Toktam Mohammadi-Moghaddam
    ;
    Mohaddeseh Kariminejad
    ;
    Hamid Bakhshabadi
    ;
    Elham Taghavi
    ;
    Afsaneh Morshedi
    To predict the oxidation parameters of soybean oil (SBO), we utilized five levels of black plum peel extraction (BPPE) antioxidant concentration (0, 400, 800, 1200, and 2000 ppm) and four levels of oil storage time (0, 8, 16, and 24 days) under accelerated oxidation conditions (temperature 60°C). We investigated the peroxide value (PV), thiobarbituric acid (TBA) value, acidity, conjugated diene (CD) content, and carbonyl value (CV). Artificial neural networks were employed using MATLAB software for prediction. Several feed-forward back-propagation networks with 2-6-5 topologies were examined, achieving correlation coefficients greater than 0.959 and mean square errors (MSE) < 0.009. The optimal model utilized a sigmoid logarithm activation function, a jumping learning pattern, and 1000 learning cycles. These models demonstrated high correlation coefficients (above 0.912) in predicting the oxidation process of SBO.
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    Modeling and Optimization of the Osmotic Dehydration of Cantaloupe
    (United Scientific Group, 2024-10-09)
    Hamid Bakhshabadi
    ;
    Mohammad Ganje
    ;
    Masoumeh Moghimi
    ;
    Alireza Ghodsvali
    ;
    Toktam Mohammadi-Moghaddam
    In current research, the optimization of osmotic dehydration of cantaloupe pieces aimed to maximize water loss (WL) and minimize moisture reabsorption using artificial neural network (ANN). The effects of three parameters were studied: osmotic solution temperature (40-60°C), immersion time (40-240 min), and solution concentration (40-60°Brix), employing central composite design (CCD). Various parameters including WL, solid gain (SG), reduction in WL to SG ratio, and reduction in sample weight were analyzed. The results indicated that the optimal conditions for osmotic dehydration were achieved with a solution temperature of 60°C, immersion time of 85.71 min, and solution concentration of 40% sucrose (sugar). Under these conditions, the following parameters were observed: WL of 3.79%, SG of 43.74%, WL to SG ratio of 14.48, and sample weight reduction of 47.71%. Furthermore, results from the ANN revealed that a network structure with one hidden layer comprising 5 nodes (3-5-4 network with 3 inputs, 5 nodes in the hidden layer, and 4 outputs) provided the most accurate predictions. This network achieved correlation coefficients (R2) of 0.999 and root mean squared error (RMSE) of 0.000039, demonstrating high reliability and precision in predicting the selected responses.