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Frying technology is one of the oldest of food preparing that it used in household and industrial scale, widely. Due to the increasing tendency of consumers to use low-fat products, efforts to reduce oil uptake in fried products has been done. Eggplant absorbs high amount of oil during frying because of high amount moisture and porous texture. In this study effect of different frying times (90, 120, 150 and 180 seconds) and cooking times (1, 4, 7 and 10 minutes) on mass transfer kinetic (oil and moisture)eggplant samples during deep fat drying and cooling period was investigated. Results of analysis variance (ANOVA) showed that independent variables on oil and moisture content was significant (p<0.05).Results of this study showed that cooking pretreatment (hot water and atmospheric pressure) 60 %, decreased oil content in comparison with which did not any pretreatment (control). In mention that cooking time 7 minutes had higher than effect on oil content decreasing (72%). To investigate the kinetic relationships, eggplant samples for 1, 3, 5, 8, 10, 12, 15, 30, 60, 75, 90, 120 and 150 seconds were fried and after each stage of oil and moisture were measured. In addition, at the end of each of these times, were immediately removed from the oil in order to measure the surface oil, immersed in ether, and the amount of oil absorbed (structural) and surface oil samples were measured.Results of mass transfer kinetic during deep fat frying at 180°C, showed that oil content fried eggplant had maximum value on the first time of deep fat frying process and then decreased. Also fried eggplant moisture content decreased, quickly and then velocity of reducing the moisture content of the samples, also declined.

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In this study, the drying process of pumpkin thin layers was investigated by cast tape drying (CTD) and convective hot air drying (CHD) methods and the effect of temperature and drying kinetics of the pumpkin was determined along with  the best mathematical model to fit the changes on moisture content to time ratio. At first, Pumpkin slices were prepared with 3, 5 and 7 mm thicknesses. Drying was performed at 75, 85 and 95 (°C) by CTD method and at 55, 65 and 75(°C) by CHT method in triplicate. Based on the kinetic model evaluated by Hii, Law and Cloke, the 7 mm thickness was selected as an optimum thickness in both drying methods. The optimal drying temperature ranges were 55 and 95 (°C) by CHD method and CTD method, respectively. Five mathematical kinetic models were fitted on the experimental data using four criteria including, Determination of Coefficient (R²), Root Mean Square Error (RMSE), Sum of Squares (SSE) and Chi-square (χ²). Also, effective diffusion coefficient (D) and activation energy (Ea) were calculated. The results showed that Hii, Law and Cloke’s model predicted the drying behavior during CTD. Activation energy of 37.5310588kJ/mol and 20.32657 kJ/mol was calculated for CHD and CTD methods respectively. The best mathematical model for drying a thin layer of pumpkin by CTD and CHD method was proposed Hii, Law and Cloke’s model.

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Vegetables are perishable and cultivated seasonally. The aim of this study was to employ a combined thawing through hot air-infrared system, while investigating the effects of temperature, airflow velocity, and infrared radiation power on thawing time and the quality attributes of thawed carrots. In this research, carrot samples, having been washed and shaped using a cylindrical mold measuring 22.5 mm in diameter and 12 mm in height, were subjected to freezing at -18°C for 48 hours. Thawing parameters were air temperature (30°C and 40°C), airflow velocity (0.5  and 5 m/s), and infrared power (100 and 300 watts). The sample thawed at 25°C was control sample. Data analysis showed that reciprocal effect of increasing temperature, power of the radiation source and air flow speed had a significant effect on the thawing time, vitamin C, β-carotene, the thawing loss, and pH (P≤0.05). This system was able to significantly reduce the thawing time this time for the control sample was 47.66 minutes and for the shortest thawing time, the treatment 8 (F5P300T40) was 6.23 minutes. The lowest pH value was related to treatment 7 (F0.5P300T40) 5.81 and the highest value was related to treatment 1(F0.5P100T30) 6.15. The highest amount of β -carotene was related to treatment number 8 (F5P300T40) 48.12 mg/100g and  the lowest amount was related to treatment 5 (F0.5P100T40) 14.03 mg/100g. The highest amount of vitamin C was related to treatment 4(F5P300T30) 12.36 mg/100g and the lowest amount was related to treatment 1(F0.5P100T30) 3.68 mg/100g. . In the thawing loss, the highest amount was related to treatment 1 (F0.5P100T30) 19.7% and the lowest amount was related to the control sample7.44%. . Due to the low start-up cost, shorter process time and favorable quality, hybrid defrosting is widely used in the food industry.

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Changing the thermos-mechanical properties, variety of formulation and storage conditions, 36 samples of low-fat mozzarella cheese were produced and their hardness, adhesiveness, cohesiveness, springiness, cohesiveness, gumminess and chewiness were evaluated by TPA followed by analyzing data using completely randomized factorial design with univariate analysis through IBM SPSS Statistics. 26. Then, Imaging of the same samples with a Hyperspectral camera in the range of 400-1000 nm as well as pre-processing the spectra and preferring the important wavelengths by feature selection algorithms to developed the calibration models including multiple linear regression algorithms, partial least squares regression, support vector machine with a linear kernel, multilayer perceptron neural network, random forests and majority voting algorithm was performed in Python software followed by the performance of models were evaluated. Results showed that the more increased the stretching time in hot water from 2 to 8 minutes, the more the hardness, springiness, gumminess and chewiness and cohesiveness increased, but adhesiveness was decreased. The majority vote algorithm (VOTING) revealed the highest performance in hardness prediction (R²p=0.878, RMSEp=2606.52 and RPD=2.12) and was able to predict the cohesiveness of mozzarella with higher accuracy more than other algorithms. Multiple linear regression couldn’t predict the adhesiveness properly, but random forest method with high performance predicted this feature (R²p=0.808, RMSE=56.49, RPD=1.90). The multi-layer perceptron neural network with the least error, predicted springiness (R²p = 0.848, RMSEp = 0.094, RPD = 2.12) and chewiness (R²p = 0.84, RMSEp = 1117.21, RPD = 1.96) with high accuracy. All methods except random forest were able to predict the gumminess of mozzarella with high efficiency. In this study, it was cleared that the process conditions had significant effects on the textural characteristics and the Hyperspectral imaging was found to be a suitable alternative method for estimating the textural characteristics of mozzarella cheese.
 

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Purpose: The purpose of the current research is to dry apples using a solar dryer with an absorbent cycle in two modes of direct-convective radiation and indirect-convective radiation. Two types of direct and indirect drying methods with and without moisture absorbers and drying in open air were used. The pre-prepared apple slices (flavored with cinnamon) were placed on a piece of mesh with certain dimensions, the initial weight of the samples was measured and inside the solar dryer with an absorbent cycle in two modes of direct-convective radiation and indirect-convective radiation. It was placed; And during the drying process, the rate of evaporation, texture, wrinkling, water reabsorption rate, retention rate of vitamin C (ascorbic acid) and color change and sensory characteristics at different levels of treatment (30% sucrose and without sucrose and zero, 0.5, 1 and 2% of cinnamon) and also some samples were dried by solar dryer without flavoring with cinnamon alone. Data analysis was done with the completely random factorial design statistical method and using SPSS version 26 statistical software.

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In this research, transfer phenomena during hot air frying and deep frying were investigated. Hot air frying (HAF) and deep frying (DFF) were carried out at 160 °C for 15 minutes for shrimp cylindrical pieces. Temperature variations at the product's core were recorded using a T-type thermocouple equipped Data Logger and PicoLog software on a computer. Moisture content and oil of the product were determined. Heat and mass transfer parameters estimate by using the logarithmic plot of dimensionless temperature against time and empirical equations. Results showed that Mass and heat transfer parameters during hot air frying were lower than deep frying method. In deep frying, the Biot number and the effective diffusion coefficient were higher than the hot air frying method. The kinetic constant of moisture reduction in the product was higher in the deep frying method.
 

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In this research, artificial neural networks (ANN) was presented to predict changes in moisture and density of shrimp during hot air frying process (at three temperatures of 140, 160 and 180 degrees Celsius for 15 minutes). Neural networks in the form of multilayer perceptron (MLP) with sigmoid tangent transfer function in the hidden layer and linear transfer function in the output layer was designed to predict moisture (with two inputs: temperature and time) and density (with three inputs: temperature, time and moisture) in MATLAB software. Different backpropagation algorithms include Levenberg-Marquardt, Gradient descent, Gradient descent with adaptive learning rate, Adaptive learning rate backpropagation, Gradient descent with momentum, and Scaled conjugate gradient. The structure of the models was validated by calculating the coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE). Finally, the importance of the inputs in terms of the effect on the output variable for predicting moisture and density was investigated by designing the default hyperbolic tangent neural networks in SPSS software. The results showed that with the decrease in moisture and the development of pores in shrimp, the density of the product gradually decreased during hot air frying, and with the increase in the temperature of the process, a further decrease in density was observed. In the moisture model, the backpropagation algorithm of Gradient descent with momentum (R²=0.989, RMSE=0.171, MAE=0.131) and in the density model, the Levenberg-Marquardt algorithm (R²=0.974, RMSE=0.0096, MAE=0.0066) showed the minimum error in testing. In the moisture neurocomputing, the importance of time and temperature variable was equal to 0.883 and 0.117, respectively. In the density neurocomputing, the importance of moisture, time and temperature variables were 0.588, 0.278 and 0.134, respectively. The Findings can be used in the design of artificial intelligence for controlling and creating automation in hot air fryers.