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Author: search.filters.author.Hamid Bakhshabadi

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    A Review of New Methods for Extracting Oil from Plants to Enhance the Efficiency and Physicochemical Properties of the Extracted Oils
    (MDPI AG, 2025-04-09)
    Hamid Bakhshabadi
    ;
    Mohammad Ganje
    ;
    Mehdi Gharekhani
    ;
    Toktam Mohammadi-Moghaddam
    ;
    Cristina Aulestia
    In general, there are three methods for extracting oil from various sources: mechanical, solvent, and pre-press-solvent. Each of these methods has its own advantages and disadvantages, with extraction efficiency depending on key factors such as the extraction technique, the properties of the plant component matrix, and the solvent used. Factors like temperature, pressure, and time also play a role. Researchers have consistently sought to replace or complement these methods to reduce residual oil in products. This study introduces new oil extraction methods that have gained attention in recent years, including the microwave, pulsed electric field, ultrasound, supercritical fluid, enzymatic, ohmic, and combined methods to enhance efficiency. The research demonstrates that these methods increase oil extraction efficiency and bioactive compound extraction from plant sources, resulting in improved oil quality. Most methods also reduce extraction time, offering researchers and industrialists a variety of options for their oil extraction needs. However, the study notes contradictions in the results, such as varying acidity levels in the oil, which may be attributed to raw materials and study conditions. In the end, it was determined that ultrasound, pulsed electric field, and enzyme methods can be used industrially to extract oil from olives, while supercritical fluid can be used to extract oil from certain seeds.
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    The Effect of Optimizing the Stripping and Drying Parameters During Industrial Extraction on the Physicochemical Properties of Soybean Oil
    (MDPI AG, 2025-02-14)
    Toktam Mohammadi-Moghaddam
    ;
    Hamid Bakhshabadi
    ;
    Abolfazl Bojmehrani
    ;
    VALDES ALARCON, MARCOS EDUARDO  
    ;
    MORSHEDI, AFSANEH  
    Soybean oil is the second most consumed vegetable oil worldwide and is recognized as a source of heart-healthy polyunsaturated fatty acids. Optimizing the extraction process in the oil industry is essential for both economic and environmental sustainability. This research aimed to determine the optimal conditions for various extraction parameters—stripper temperature (110–140 °C), stripper pressure (150–210 mbar), and dryer pressure (60–120 mbar)—and their effects on the physicochemical properties of soybean oil. These properties include oil-insoluble fine substances, acidity, the color index, peroxide value, oxidative stability, and moisture content. The results indicated that the stripper temperature significantly influenced oil-insoluble fine substances, acidity, the color index, and peroxide value (p < 0.05). The optimal conditions for oil extraction were found to be a stripper temperature of 110 °C, a stripper pressure of 150 mbar, and a dryer pressure of 120 mbar. Under these conditions, the oil-insoluble fine substances, acidity, the color index, peroxide value, oxidative stability, and moisture content of soybean oil were in the ranges of 0.2–0.58%, 0.63–1.15%, 4.3–5.5, 0.67–1.23 meqO2/kg, 3–5.5, and 0.05–0.11%, respectively. These findings provide valuable insight for optimizing soybean oil extraction processes to enhance quality and efficiency. Future advancements in industrial oil extraction are expected to focus on integrating efficient, eco-friendly technologies and enhancing precision through automation and data analytics to optimize yield and minimize waste.
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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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    Application of Artificial Neural Networks for Predicting Cooking Dynamics in Industrial Sesame Seed Oil Extraction
    (United Scientific Group, 2024-10-01)
    Hamid Bakhshabadi
    ;
    Alireza Ghodsvali
    ;
    Abolfazl Bojmehrani
    ;
    Mohammad Ganje
    ;
    Toktam Mohammadi-Moghaddam
    Sesame seeds are a significant source of vegetable oil and were among the earliest grains used for oil extraction. In this study, aimed at designing an industrial-scale process for extracting oil from sesame seeds, we investigated three cooking temperatures (75°C, 90°C, and 105°C) and three different moisture contents of the seeds leaving the cooking pot (4.5%, 5.5%, and 6.5%). The study focused on several responses: the oil content of the pressed cake, the quantity of extracted oil, the protein and moisture contents of the resulting meal, and the percentage of insoluble fine particles in the extracted oil. To predict these responses, an artificial neural network (ANN) model was employed. Among the various backpropagation feedforward networks with different topologies studied, the configuration with 2 input nodes, 5 hidden nodes in one layer, and 5 output nodes was selected based on its high correlation coefficient (R² = 0.997) and low mean squared error (MSE = 0.0002). The sigmoid hyperbolic tangent activation function was used, and the Levenberg-Marquardt learning algorithm with 1000 learning cycles was identified as the optimal neural model. The selected optimized models demonstrated high R² ≥ 0.97 during the evaluation of their results.
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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.