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Optimization of Partial Replacement of Wheat Flour with Black Plum Peel Powder and Citric Acid in Sponge Cake: Effects on Physicochemical, Sensory, and Antioxidant Properties
(MDPI AG, 2026-02-17)
Toktam Mohammadi-Moghaddam
;
Oscar Santacoloma-Perez
;
MORSHEDI, AFSANEH
Black plum peel is a by-product of the processing of black plum and contains nutritional properties of black plum that can be utilized in the production of innovative food products. In this study, different percentages of black plum peel powder (5, 10, and 15%) and citric acid (0, 1 and 2%) were used in sponge cake formulation and physico-chemical properties (moisture content, acidity, pH), antioxidant capacity, cooking loss, texture characteristics (hardness, cohesiveness, gumminess, springiness, resilience and adhesiveness) and sensory attributes (hardness, adhesiveness, porosity, springiness and overall acceptance) were evaluated using Response Surface Methodology. Increasing the levels of black plum peel powder and citric acid resulted in higher moisture content, acidity, antioxidant capacity, cohesiveness, resilience and overall acceptance of the black plum peel cake, while the pH decreased. The linear effect of citric acid had the most significant influence on moisture content, acidity, antioxidant capacity, sensory hardness, adhesiveness and overall acceptance whereas pH, cohesiveness, and resilience were most influenced by the linear term of black plum peel powder. Porosity and springiness were impacted with quadratic parameter of citric acid and cooking loss was affected by the quadratic term of black plum peel powder. Response Surface Methodology proved to be an effective tool for optimizing the sponge cake formulation, with high model adequacy indicated by R2 values of 0.92–0.99, non-significant lack-of-fit (p > 0.05), and adequate precision > 10, identifying 2% citric acid and 11.56% black plum peel powder as the optimal concentrations. Compared to control cake, the optimized formulation showed slightly higher moisture content, significantly enhanced antioxidant capacity, and similar or improved overall acceptability, demonstrating its functional and sensory advantages. The black plum sponge cake is an economical, enriched cake with higher quality that utilizes a by-product of the plum industry.
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Valorization of Black Plum Peel in Spread Formulation: Optimization of Physicochemical and Sensory Properties via RSM
(MDPI AG, 2025-12-23)
Toktam Mohammadi-Moghaddam
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LARREA PAREDES, MARÍA DE LOURDES
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MORSHEDI, AFSANEH
Plum peel is a major by-product of plum processing and a rich source of nutrients and bioactive compounds. This study aimed to optimize a black plum peel spread formulated with apple puree (20–40%) and plum puree (10–30%) using response surface methodology (RSM). Increasing apple puree up to 30% reduced acidity, firmness, cohesiveness, and consistency while improving sourness and overall acceptability. At 40%, apple puree decreased total acceptability and sourness while slightly increasing texture parameters. Increasing plum puree up to 20% lowered acidity, firmness, consistency, cohesiveness, viscosity, and sourness, but further increases to 30% reversed these effects. Both apple and plum purees enhanced antioxidant capacity in a concentration-dependent manner. The interaction between apple and plum purees notably affected the viscosity of the spread. Overall, plum puree had the strongest influence on texture and color, while apple puree primarily affected sensory acceptance. The optimal formulation was 32.01% apple puree and 28.16% plum puree (R2 = 0.999). Developing a spread from black plum peel demonstrates a sustainable strategy for upcycling fruit-processing waste into nutritious, high-value products. This approach reduces environmental impact, supports circular food production, and creates new opportunities for functional spreads in the health-oriented food markets.
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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
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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
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Hamid Bakhshabadi
;
Abolfazl Bojmehrani
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VALDES ALARCON, MARCOS EDUARDO
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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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Bread Staling Measurement Techniques: A Review
(United Scientific Group, 2024-05-31)
Toktam Mohammadi-Moghaddam
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Mohammad Morshedi
;
Ramina Moalemzadeh Ansari
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Amir Golmohammadi
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Afsaneh Morshedi
The global production volume of wheat production is almost 785 million metric tons during 2023. Bread is a staple food produced with flour, water, or milk, with or without yeast. It is an important part of people’s worldwide people diet and provides their daily energy. One of the most significant problems in bread production and consumption is its staleness, which begins immediately after baking. Staling is divided into two categories: texture and microbial changes. Texture changes occur first due to moisture migration, followed by microbial changes. Ultimately, the aroma, flavor, and texture of bread become undesirable to consumers. There are many methods for evaluation staling: chemical, macroscopic, rheological, microscopic, structural features, and molecular features methods. All these methods are effective and can be used according to the facilities and conditions of the producers.
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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
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Mohaddeseh Kariminejad
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Hamid Bakhshabadi
;
Elham Taghavi
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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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Review of Some Thermal Methods in Drying and Roasting Processes
(United Scientific Group, 2024-09-18)
Toktam Mohammadi-Moghaddam
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Mohammad Morshedi
;
Ramina Moalemzadeh Ansari
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Afsaneh Morshedi
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VALDES ALARCON, MARCOS EDUARDO
Thermal processing is a routine procedure in food science, with two important methods being drying and roasting. During thermal processing, simultane-ous heat and mass transfer occur, where the distribution of heat and humidity depends on effective diffusivity. Various methods exist for achieving this, each differing in efficiency and energy consumption. The conventional method of thermal processing involves hot air (HA) or convection, which typically requires sig-nificantly more energy and time (at least 25%). However, there are newer thermal processing methos based on radiation, each with their own advantages and disad-vantages. Nevertheless, all radiation-based methods generally consume less time and energy compared to the HA method. Different thermal processing methods have been studied and reviewed with regard to their energy consumption and effective diffusivity. In summary, while HA remains the routine method in indus-tries, it demands considerably more energy and time compared to radiation-based methods. Radiofrequency is a non-thermal method that can also be employed to enhance the efficiency of various processing techniques.
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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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A Simple Overview in Magnetic Resonance Imaging Application in Evaluation of Food Quantity and Quality Aspects
(United Scientific Group, 2024-10-03)
Toktam Mohammadi-Moghaddam
;
Mohammad Morshedi
;
Afsaneh Morshedi
;
VALDES ALARCON, MARCOS EDUARDO
Magnetic resonance imaging (MRI) has been a professional method in medical diagnostics for many years. Recently, considering the increase in population, preparing healthy food is a worldwide challenge. Hypothesis and implementation of MRI in research of food is approximately new. MRI is considered as a green, noninvasive, low cost, rapid, and nondestructive experimental method for investigating food processing. This method could be used in a short time while its results are suitable to apply in different industries even in online monitoring. Utilizing MRI techniques enhances the capacity to quantify basic processes such as gelation, crystallization, drying, dehydration, freezing, diffusion, and flow that occur in food products. This technology equips food scientists with a robust tool in the physicochemical properties study of food systems or specified food components and assessing them throughout diverse processes. This technique has some disadvantages in some process conditions too.
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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.

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