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Ebrahim Taghinezhad
Department of Agricultural Engineering and Technology, Moghan College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran

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Journal article
Published: 26 April 2021 in Sensors
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In this study, the possibility of non-destructive detection of tomato pesticide residues was investigated using Vis/NIRS and prediction models such as PLSR and ANN. First, Vis/NIR spectral data from 180 samples of non-pesticide tomatoes (used as a control treatment) and samples impregnated with pesticide with a concentration of 2 L per 1000 L between 350–1100 nm were recorded by a spectroradiometer. Then, they were divided into two parts: Calibration data (70%) and prediction data (30%). Next, the prediction performance of PLSR and ANN models after processing was compared with 10 spectral preprocessing methods. Spectral data obtained from spectroscopy were used as input and pesticide values obtained by gas chromatography method were used as output data. Data dimension reduction methods (principal component analysis (PCA), Random frog (RF), and Successive prediction algorithm (SPA)) were used to select the number of main variables. According to the values obtained for root-mean-square error (RMSE) and correlation coefficient (R) of the calibration and prediction data, it was found that the combined model SPA-ANN has the best performance (RC = 0.988, RP = 0.982, RMSEC = 0.141, RMSEP = 0.166). The investigational consequences obtained can be a reference for the development of internal content of agricultural products, based on NIR spectroscopy.

ACS Style

Araz Nazarloo; Vali Sharabiani; Yousef Gilandeh; Ebrahim Taghinezhad; Mariusz Szymanek. Evaluation of Different Models for Non-Destructive Detection of Tomato Pesticide Residues Based on Near-Infrared Spectroscopy. Sensors 2021, 21, 3032 .

AMA Style

Araz Nazarloo, Vali Sharabiani, Yousef Gilandeh, Ebrahim Taghinezhad, Mariusz Szymanek. Evaluation of Different Models for Non-Destructive Detection of Tomato Pesticide Residues Based on Near-Infrared Spectroscopy. Sensors. 2021; 21 (9):3032.

Chicago/Turabian Style

Araz Nazarloo; Vali Sharabiani; Yousef Gilandeh; Ebrahim Taghinezhad; Mariusz Szymanek. 2021. "Evaluation of Different Models for Non-Destructive Detection of Tomato Pesticide Residues Based on Near-Infrared Spectroscopy." Sensors 21, no. 9: 3032.

Journal article
Published: 01 April 2021 in Molecules
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Most agricultural products are harvested with a moisture content that is not suitable for storage. Therefore, the products are subjected to a drying process to prevent spoilage. This study evaluates an infrared rotary dryer (IRRD) with three levels of infrared power (250, 500, and 750 W) and three levels of rotation speed (5, 10, and 15 rpm) to dry terebinth. Response surface methodology (RSM) was used to illustrate and optimize the interaction between the independent variables (infrared power and rotation speed) and the response variables (drying time, moisture diffusivity, shrinkage, color change, rehydration rate, total phenolic content, and antioxidant activity). As infrared power and rotation speed increased, drying time, rehydration rate, antioxidant activity, and total phenolic content decreased, while the other parameters were increased. According to the results, the optimum drying conditions of terebinth were determined in the IRRD at an infrared power of 250 W and drum rotation speed of 5 rpm. The optimum values of the response variables were 49.5 min for drying time, 8.27 × 10−9 m2/s for effective moisture diffusivity, 2.26 for lightness, 21.60 for total color changes, 34.75% for shrinkage, 2.4 for rehydration rate, 124.76 mg GAE/g d.m. for total phenolic content and 81% for antioxidant activity.

ACS Style

Mohammad Kaveh; Yousef Abbaspour-Gilandeh; Ebrahim Taghinezhad; Dorota Witrowa-Rajchert; Małgorzata Nowacka. The Quality of Infrared Rotary Dried Terebinth (Pistacia atlantica L.)-Optimization and Prediction Approach Using Response Surface Methodology. Molecules 2021, 26, 1999 .

AMA Style

Mohammad Kaveh, Yousef Abbaspour-Gilandeh, Ebrahim Taghinezhad, Dorota Witrowa-Rajchert, Małgorzata Nowacka. The Quality of Infrared Rotary Dried Terebinth (Pistacia atlantica L.)-Optimization and Prediction Approach Using Response Surface Methodology. Molecules. 2021; 26 (7):1999.

Chicago/Turabian Style

Mohammad Kaveh; Yousef Abbaspour-Gilandeh; Ebrahim Taghinezhad; Dorota Witrowa-Rajchert; Małgorzata Nowacka. 2021. "The Quality of Infrared Rotary Dried Terebinth (Pistacia atlantica L.)-Optimization and Prediction Approach Using Response Surface Methodology." Molecules 26, no. 7: 1999.

Journal article
Published: 01 February 2021 in Applied Sciences
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The present study examined the effect of ultrasonic pretreatment at three time the levels of 10, 20 and 30 min on some thermodynamic (effective moisture diffusivity coefficient(Deff ), drying time, specific energy consumption (SEC), energy efficiency, drying efficiency, and thermal efficiency) and physical (color and shrinkage) properties of kiwifruit under hybrid hot air-infrared(HAI) dryer at different temperatures (50, 60 and 70 °C) and different thicknesses (4, 6 and 8 mm). A total of 11 mathematical models were applied to represent the moisture ratio (MR) during the drying of kiwifruit. The fitting of MR mathematical models to experimental data demonstrated that the logistic model can satisfactorily describe the MR curve of dried kiwifruit with a correlation coefficient (R 2) of 0.9997, root mean square error (RMSE) of 0.0177 and chi-square (χ 2) of 0.0007. The observed Deff of dried samples ranged from 3.09 × 10−10 to 2.26 × 10−9 m2/s. The lowest SEC, color changes and shrinkage were obtained as 36.57 kWh/kg, 13.29 and 25.25%, respectively. The highest drying efficiency, energy efficiency, and thermal efficiency were determined as 11.09%, 7.69% and 10.58%, respectively. The results revealed that increasing the temperature and ultrasonic pretreatment time and decreasing the sample thickness led to a significant increase (p < 0.05) in drying efficiency, thermal efficiency, and energy efficiency, while drying time, SEC and shrinkage significantly decreased (p < 0.05).

ACS Style

Ebrahim Taghinezhad; Mohammad Kaveh; Antoni Szumny. Thermodynamic and Quality Performance Studies for Drying Kiwi in Hybrid Hot Air-Infrared Drying with Ultrasound Pretreatment. Applied Sciences 2021, 11, 1297 .

AMA Style

Ebrahim Taghinezhad, Mohammad Kaveh, Antoni Szumny. Thermodynamic and Quality Performance Studies for Drying Kiwi in Hybrid Hot Air-Infrared Drying with Ultrasound Pretreatment. Applied Sciences. 2021; 11 (3):1297.

Chicago/Turabian Style

Ebrahim Taghinezhad; Mohammad Kaveh; Antoni Szumny. 2021. "Thermodynamic and Quality Performance Studies for Drying Kiwi in Hybrid Hot Air-Infrared Drying with Ultrasound Pretreatment." Applied Sciences 11, no. 3: 1297.

Journal article
Published: 31 January 2021 in Foods
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Drying can prolong the shelf life of a product by reducing microbial activities while facilitating its transportation and storage by decreasing the product weight and volume. The quality factors of the drying process are among the important issues in the drying of food and agricultural products. In this study, the effects of several independent variables such as the temperature of the drying air (50, 60, and 70 °C) and the thickness of the samples (2, 4, and 6 mm) were studied on the response variables including the quality indices (color difference and shrinkage) and drying factors (drying time, effective moisture diffusivity coefficient, specific energy consumption (SEC), energy efficiency and dryer efficiency) of the turnip slices dried by a hybrid convective-infrared (HCIR) dryer. Before drying, the samples were treated by three pretreatments: microwave (360 W for 2.5 min), ultrasonic (at 30 °C for 10 min) and blanching (at 90 °C for 2 min). The statistical analyses of the data and optimization of the drying process were achieved by the response surface method (RSM) and the response variables were predicted by the adaptive neuro-fuzzy inference system (ANFIS) model. The results indicated that an increase in the dryer temperature and a decline in the thickness of the sample can enhance the evaporation rate of the samples which will decrease the drying time (40–20 min), SEC (from 168.98 to 21.57 MJ/kg), color difference (from 50.59 to 15.38) and shrinkage (from 67.84% to 24.28%) while increasing the effective moisture diffusivity coefficient (from 1.007 × 10−9 to 8.11 × 10−9 m2/s), energy efficiency (from 0.89% to 15.23%) and dryer efficiency (from 2.11% to 21.2%). Compared to ultrasonic and blanching, microwave pretreatment increased the energy and drying efficiency; while the variations in the color and shrinkage were the lowest in the ultrasonic pretreatment. The optimal condition involved the temperature of 70 °C and sample thickness of 2 mm with the desirability above 0.89. The ANFIS model also managed to predict the response variables with R 2 > 0.96.

ACS Style

Ebrahim Taghinezhad; Mohammad Kaveh; Antoni Szumny. Optimization and Prediction of the Drying and Quality of Turnip Slices by Convective-Infrared Dryer under Various Pretreatments by RSM and ANFIS Methods. Foods 2021, 10, 284 .

AMA Style

Ebrahim Taghinezhad, Mohammad Kaveh, Antoni Szumny. Optimization and Prediction of the Drying and Quality of Turnip Slices by Convective-Infrared Dryer under Various Pretreatments by RSM and ANFIS Methods. Foods. 2021; 10 (2):284.

Chicago/Turabian Style

Ebrahim Taghinezhad; Mohammad Kaveh; Antoni Szumny. 2021. "Optimization and Prediction of the Drying and Quality of Turnip Slices by Convective-Infrared Dryer under Various Pretreatments by RSM and ANFIS Methods." Foods 10, no. 2: 284.

Journal article
Published: 21 January 2021 in Processes
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The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

ACS Style

Araz Soltani Nazarloo; Vali Rasooli Sharabiani; Yousef Abbaspour Gilandeh; Ebrahim Taghinezhad; Mariusz Szymanek; Maciej Sprawka. Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes. Processes 2021, 9, 196 .

AMA Style

Araz Soltani Nazarloo, Vali Rasooli Sharabiani, Yousef Abbaspour Gilandeh, Ebrahim Taghinezhad, Mariusz Szymanek, Maciej Sprawka. Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes. Processes. 2021; 9 (2):196.

Chicago/Turabian Style

Araz Soltani Nazarloo; Vali Rasooli Sharabiani; Yousef Abbaspour Gilandeh; Ebrahim Taghinezhad; Mariusz Szymanek; Maciej Sprawka. 2021. "Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes." Processes 9, no. 2: 196.

Research article
Published: 25 December 2020 in Food Science & Nutrition
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The measurement of different quality properties requires particular tools and chemical materials, most of which are time‐using. The present research was accomplished to survey the possibility of using NIRS (870–2450 nm) to predict the amylose content (AC), protein content (PC), breakdown (BDV), and setback viscosity (SBV) of white rice (Khazar variety) and its flour. Determination coefficients of calibration models to flour samples of AC, PC, BDV, and SBV generated by the partial least‐squares (PLS) regression were obtained as R2cal ≥ .85 and R2pre ≥ .80. Root mean square error of calibration (RMSEC) was calculated as 0.393, 0.07, 2.55, and 1.33, respectively. Similarly to grain samples, were obtained as R2cal ≥ .88 and R2pre ≥ .71 for calibration and prediction. RMSEC was measured as 0.303, 0.27, 2.59, and 3.11, respectively. NIRS has the potential to be used as a quick technique for predicting the quality attributes of kernel specimens.

ACS Style

Nasrollah Fazeli Burestan; Amir Hossein Afkari Sayyah; Ebrahim Taghinezhad. Prediction of some quality properties of rice and its flour by near‐infrared spectroscopy (NIRS) analysis. Food Science & Nutrition 2020, 9, 1099 -1105.

AMA Style

Nasrollah Fazeli Burestan, Amir Hossein Afkari Sayyah, Ebrahim Taghinezhad. Prediction of some quality properties of rice and its flour by near‐infrared spectroscopy (NIRS) analysis. Food Science & Nutrition. 2020; 9 (2):1099-1105.

Chicago/Turabian Style

Nasrollah Fazeli Burestan; Amir Hossein Afkari Sayyah; Ebrahim Taghinezhad. 2020. "Prediction of some quality properties of rice and its flour by near‐infrared spectroscopy (NIRS) analysis." Food Science & Nutrition 9, no. 2: 1099-1105.

Journal article
Published: 28 July 2020 in Fuel
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The failure of classical techniques and algorithms have triggered researchers to search for stochastic tools capably of exploring the search space with constant convergence speed. Grey wolf optimizer (GWO) is a moderately novel stochastic algorithm with only a few parameters to regulate that can be easily employed for global optimization. For the first time, this study explored GWO to model biodiesel yield. It is worthy to note that, kerosene (KS) popularly known as paraffin oil has gained global attention in the aviation and biodiesel industries to improve cold flow properties and can be mixed with diesel in different proportions. Therefore, in this study, (1) Response Surface Methodology (RSM) and GWO were explored to model the waste sunflower oil methyl ester (WSOME)/biodiesel (BD) production from waste sunflower oil (WSO) and (2) Least square regression method was accosted to correlate the density of (0%KS + 100%BD), (5PKS + 95%BD), (20%KS + 80% BD), (50%KS + 50%BD) and (100%KS + BD) blends. The yield of WSOME (96.70%) was optimum at the methanol/oil molar ratio of 5.99/1, catalyst amount of 1.1 wt.% and reaction time of 77.6 min. The GWO model displayed a higher coefficient of determination, and a lower value of root mean squared errors compared to the RSM model. GWO predicted values, as compared to RSM, predicted yield indicates its reliability and usefulness for prediction without trial and error of conventional experimentation. The fuel properties concurred with the ranges of the ASTMD6751 and EN 14214 specifications. The quadratic relation with high regression coefficient (R2) was detected for the densities of (0%KS+100%BD) and (20%KS +80% BD) while the linear was found suitable for the densities of (5KS + 95%BD), (50%KS +50BD) and (100%KS+0%BD). The model and correlations can find application in biodiesel and aviation industries.

ACS Style

Olusegun David Samuel; Modestus Okwu; Oluwayomi J. Oyejide; Ebrahim Taghinezhad; Asif Afzal; Mohammad Kaveh. Optimizing biodiesel production from abundant waste oils through empirical method and grey wolf optimizer. Fuel 2020, 281, 118701 .

AMA Style

Olusegun David Samuel, Modestus Okwu, Oluwayomi J. Oyejide, Ebrahim Taghinezhad, Asif Afzal, Mohammad Kaveh. Optimizing biodiesel production from abundant waste oils through empirical method and grey wolf optimizer. Fuel. 2020; 281 ():118701.

Chicago/Turabian Style

Olusegun David Samuel; Modestus Okwu; Oluwayomi J. Oyejide; Ebrahim Taghinezhad; Asif Afzal; Mohammad Kaveh. 2020. "Optimizing biodiesel production from abundant waste oils through empirical method and grey wolf optimizer." Fuel 281, no. : 118701.

Original research
Published: 14 June 2020 in Food Science & Nutrition
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One of the major problems in predicting the quality properties of rice is that conducting experiments in the food industry can be highly expensive. The objective of this study was to predict some quality properties in varieties (Domsiah, Hashemi, Dorfak , and Kadus ) via compression test at moisture levels 9 and 14% w.b. Based on historical data design, RSM was used to model and estimate of dependent variables (amylose (AC) and protein content (PC), gelatinization temperature, gel consistency GC), minimum (Min.V), final (FV), breakdown (BDV) and setback viscosity (SBV), peak time (PT) and pasting temperature (Pa.T)) through independent variables (the rate of force, deformation, rupture energy, tangent, and secant modulus). An ANOVA test showed that models were significant (p < 0.05). The most appropriate model for response variables prediction of AC and GC (Kadus 14%), PC (Domsiah 9%), Min.V, FV, and SBV (Dorfak 9%), BDV (Dorfak 14%), PT (Hashemi 14%), and Pa.T (Kadus 9%) was as 0.86, 0.85, 0.93, 0.955, 0.953, 0.94, 0.94, 0.86, and 0.91, respectively, with the most appropriate optimal values as 23.52%, 48, 10%, 164.95 RVU, 304.12 RVU, 162.66 RVU, 64.52 RVU, 6.09 min, and 92.45°C and desirability as 0.91, 0.95, 0.95, 0.80, 0.89, 0.83, 0.84, 0.89, and 0.96, respectively. The optimal values of the independent variables have a decreasing trend, and the optimal values of the response variables are proportional to the optimal conditions. The results indicated that the RSM could be quite useful in the optimization of the models developed for predicting the rice quality properties.

ACS Style

Nasrollah Fazeli Burestan; Amir Hossein Afkari Sayyah; Ebrahim Taghinezhad. Mathematical modeling for the prediction of some quality parameters of white rice based on the strength properties of samples using response surface methodology (RSM). Food Science & Nutrition 2020, 8, 4134 -4144.

AMA Style

Nasrollah Fazeli Burestan, Amir Hossein Afkari Sayyah, Ebrahim Taghinezhad. Mathematical modeling for the prediction of some quality parameters of white rice based on the strength properties of samples using response surface methodology (RSM). Food Science & Nutrition. 2020; 8 (8):4134-4144.

Chicago/Turabian Style

Nasrollah Fazeli Burestan; Amir Hossein Afkari Sayyah; Ebrahim Taghinezhad. 2020. "Mathematical modeling for the prediction of some quality parameters of white rice based on the strength properties of samples using response surface methodology (RSM)." Food Science & Nutrition 8, no. 8: 4134-4144.

Original research
Published: 08 June 2020 in Food Science & Nutrition
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This research examines the impact of various pretreatments on effective moisture diffusivity coefficient (Deff), activation energy (Ea), specific energy consumption (SEC ), color, and shrinkage of blackberry (Rubus spp.). Hot air drying experiments were conducted under three different temperatures (50, 60, and 70°C) and four pretreatments, including thermal pretreatment by hot water blanching at 70, 80, and 90°C, pulse pretreatment with microwave having power of 90, 180, and 360 W, chemical pretreatment using ascorbic acid (1% in distilled water), and mechanical pretreatment using ultrasonic vibration with working frequency of 28 ± 5% kHz for 15, 30, and 45 min. The results show that the highest Deff value, which was 1.00 × 10–8 m2/s, could be achieved by using a microwave pretreatment with power and drying temperature of 360 W and 70°C͘, respectively. Moreover, the lowest Deff value obtained from this similar pretreatment condition was 3.10 × 10–9 m2/s at a drying temperature of 50°C, while Ea ranged from 13.61 to 26.02 kJ/mol. The highest and lowest SECs were 269.91 kW hr/kg for the control sample and 75.63 kW hr/kg for the microwave pretreatment, respectively. Furthermore, the largest color change and shrinkage were detected in ascorbic acid pretreatment and control sample, respectively.

ACS Style

Mohammad Kaveh; Ebrahim Taghinezhad; Muhammad Aziz. Effects of physical and chemical pretreatments on drying and quality properties of blackberry ( Rubus spp.) in hot air dryer. Food Science & Nutrition 2020, 8, 3843 -3856.

AMA Style

Mohammad Kaveh, Ebrahim Taghinezhad, Muhammad Aziz. Effects of physical and chemical pretreatments on drying and quality properties of blackberry ( Rubus spp.) in hot air dryer. Food Science & Nutrition. 2020; 8 (7):3843-3856.

Chicago/Turabian Style

Mohammad Kaveh; Ebrahim Taghinezhad; Muhammad Aziz. 2020. "Effects of physical and chemical pretreatments on drying and quality properties of blackberry ( Rubus spp.) in hot air dryer." Food Science & Nutrition 8, no. 7: 3843-3856.

Articles
Published: 23 May 2020 in Drying Technology
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In this study, prediction and analysis of energy and exergy in a combined hot air-infrared dryer with ultrasound pretreatment for organic blackberry was carried out. The effect on product color and greenhouse gas (GHG) emission was assessed. To predict energy and exergy parameters such as energy utilization ratio, energy utilization, exergy loss, and exergy efficiency, both the artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) methods were employed. Drying experiments were undertaken at three temperature levels of 50, 60, and 70 °C in air speed of 1 m/s and ultrasound pretreatment time 15, 30, and 45 min, as compared to controlled samples (without pretreatment). Results demonstrated that by raising the inlet air temperature and ultrasound pretreatment time, color change rate decreased, while energy utilization and exergy efficiency increased. Energy and exergy prediction results by means of ANN and ANFIS methods showed that ANFIS method achieved a higher R2 and lower RMS as compared to ANN. The highest level of GHG emission (NOx, CO2) was obtained at 50 °C temperature for samples without pretreatment.

ACS Style

Ebrahim Taghinezhad; Mohammad Kaveh; Esmail Khalife; Guangnan Chen. Drying of organic blackberry in combined hot air-infrared dryer with ultrasound pretreatment. Drying Technology 2020, 1 -17.

AMA Style

Ebrahim Taghinezhad, Mohammad Kaveh, Esmail Khalife, Guangnan Chen. Drying of organic blackberry in combined hot air-infrared dryer with ultrasound pretreatment. Drying Technology. 2020; ():1-17.

Chicago/Turabian Style

Ebrahim Taghinezhad; Mohammad Kaveh; Esmail Khalife; Guangnan Chen. 2020. "Drying of organic blackberry in combined hot air-infrared dryer with ultrasound pretreatment." Drying Technology , no. : 1-17.

Original article
Published: 23 March 2020 in Journal of Food Processing and Preservation
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The aim of this study is to determine the kinetics and quality properties of pistachios under different microwave powers (270, 450, and 630 W) and ultrasound (US) pretreatment at 0, 10, 20, 40, and 60 min. It is also aimed at comparing the estimations of the moisture ratio of pistachio by mathematical modeling, artificial neural networks (ANNs), and adaptive neuro‐fuzzy inference system (ANFIS) methods. The minimum and maximum values of effective moisture diffusivity () were 1.43 × 10–8 and 4.30 × 10–8 m2/s, respectively. Specific energy consumption () of the samples was varied between 58.097 and 122.21 kWh/kg. The lowest value of shrinkage (13.1%) and color change (19.97) were reported in a microwave power of 270 W and duration of US 0 min. The results showed that considering ( = .9997) and Mean Square Error ( = 0.0004), the ANFIS model had a better performance in predicting the moisture ratio of pistachios compared to the mathematical models and ANNs. Practical applications Drying is defined as the process of removing moisture through the simultaneous transfer of heat and mass. Heat transfer from the surrounding environment to the foodstuff leads to the evaporation of the surface moisture. Moisture can also be transferred from inside the product to its surface and then evaporate there. The use of US waves has been suggested in combination drying methods especially in situations where preserving the appearance and nutritional value of the food are the main priority. The effective moisture diffusion coefficient is one of the important parameters in modeling, designing, and optimizing the drying process. Color is an important indicator of the quality of the food and represents the chemical, biochemical, and microbiological characteristics of the product. During the drying process, due to the evaporation of moisture from the foodstuff, the phenomenon of shrinkage occurs and this also affects the physical properties of the solids and the appearance of the final product.

ACS Style

Ahmad Jahanbakhshi; Mohammad Kaveh; Ebrahim Taghinezhad; Vali Rasooli Sharabiani. Assessment of kinetics, effective moisture diffusivity, specific energy consumption, shrinkage, and color in the pistachio kernel drying process in microwave drying with ultrasonic pretreatment. Journal of Food Processing and Preservation 2020, 44, e14449 .

AMA Style

Ahmad Jahanbakhshi, Mohammad Kaveh, Ebrahim Taghinezhad, Vali Rasooli Sharabiani. Assessment of kinetics, effective moisture diffusivity, specific energy consumption, shrinkage, and color in the pistachio kernel drying process in microwave drying with ultrasonic pretreatment. Journal of Food Processing and Preservation. 2020; 44 (6):e14449.

Chicago/Turabian Style

Ahmad Jahanbakhshi; Mohammad Kaveh; Ebrahim Taghinezhad; Vali Rasooli Sharabiani. 2020. "Assessment of kinetics, effective moisture diffusivity, specific energy consumption, shrinkage, and color in the pistachio kernel drying process in microwave drying with ultrasonic pretreatment." Journal of Food Processing and Preservation 44, no. 6: e14449.

Journal article
Published: 07 March 2020 in Journal of Cleaner Production
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Today, global warming as a consequence of consuming fossil fuels has become a global concern. Fossil fuels used in power plants to generate power have the highest contribution to the emission of greenhouse gases (GHG) worldwide. Considering the large share of the agriculture sector in power consumption, the processing and drying industries account for the highest energy consumption in this sector. Formation and emission of GHG are associated with farm practices. These emissions are more important in the drying process because it is required large amounts of energy. This study examined GHG emissions (NOX, CO2 and SO2) during the drying of pistacia atlantica samples using 5 different types of dryers, namely hot air (HA), hybrid hot air–Infrared (HA–IR), hybrid hot air–microwave (HA–MW), continuous multistage (CMS) conveyor dryer, and hybrid collector-equipped hot air - solar (HA–Solar) dryers. Different types of turbines (steam, gas, and combined) running on natural gas, heavy oil and gasoil were used to supply their energy requirements. Results indicated that the highest (340.97 kWh/kg) and lowest (6.01 kWh/kg) specific energy consumption (SEC) occurred in CMS and HA–MW dryers, respectively. In general, the highest NOx, CO2 and SO2 emissions were 357336.6, 1974.21 and 5210.02 g, respectively, in the continuous dryer at 40 °C using an air velocity of 1.5 m/s and a conveyor speed of 10.5 mm/s for one kg of dried crop. The lowest NOx, CO2 and SO2 emissions were also 2704.5, 11.47 and 0 g, respectively, in the HA–MW dryer at 70 °C using an air velocity of 0.5 m/s exposed to 630 W of microwave power for one kg of dried crop. The experimental results showed that GHG emissions for all dryers were reduced with the increase in the air temperature and reduction in the inlet air velocity. In the IR, MW and CMS, GHG emissions were lower at an increased IR power, increased MW power, and low conveyor speed, respectively.

ACS Style

Mohammad Kaveh; Reza Amiri Chayjan; Ebrahim Taghinezhad; Vali Rasooli Sharabiani; Ali Motevali. Evaluation of specific energy consumption and GHG emissions for different drying methods (Case study: Pistacia Atlantica). Journal of Cleaner Production 2020, 259, 120963 .

AMA Style

Mohammad Kaveh, Reza Amiri Chayjan, Ebrahim Taghinezhad, Vali Rasooli Sharabiani, Ali Motevali. Evaluation of specific energy consumption and GHG emissions for different drying methods (Case study: Pistacia Atlantica). Journal of Cleaner Production. 2020; 259 ():120963.

Chicago/Turabian Style

Mohammad Kaveh; Reza Amiri Chayjan; Ebrahim Taghinezhad; Vali Rasooli Sharabiani; Ali Motevali. 2020. "Evaluation of specific energy consumption and GHG emissions for different drying methods (Case study: Pistacia Atlantica)." Journal of Cleaner Production 259, no. : 120963.

Journal article
Published: 13 January 2020 in Foods
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The effect of hybrid infrared-convective (IRC), microwave (MIC) and infrared-convective-microwave (IRCM) drying methods on thermodynamic (drying kinetics, effective moisture diffusivity coefficient (Deff), specific energy consumption (SEC)) and quality (head rice yield (HRY), color value and lightness) characteristics of parboiled rice samples were investigated in this study. Experimental data were fitted into empirical drying models to explain moisture ratio (MR) variations during drying. The Artificial Neural Network (ANN) method was applied to predict MR. The IRCM method provided shorter drying time (reduce percentage = 71%) than IRC (41%) and microwave (69%) methods. The Deff of MIC drying (6.85 × 10−11–4.32 × 10−10 m2/s) was found to be more than the observed in IRC (1.32 × 10−10–1.87 × 10−10 m2/s) and IRCM methods (1.58 × 10−11–2.31 × 10−11 m2/s). SEC decreased during drying. Microwave drying had the lowest SEC (0.457 MJ/kg) compared to other drying methods (with mean 28 MJ/kg). Aghbashlo’s model was found to be the best for MR prediction. According to the ANN results, the highest determination coefficient (R2) values for MR prediction in IRC, IRCM and MIC drying methods were 0.9993, 0.9995 and 0.9990, respectively. The HRY (from 60.2 to 74.07%) and the color value (from 18.08 to 19.63) increased with the drying process severity, thereby decreasing the lightness (from 57.74 to 62.17). The results of this research can be recommended for the selection of the best dryer for parboiled paddy. Best drying conditions in the study is related to the lowest dryer SEC and sample color value and the highest HRY and sample lightness.

ACS Style

Ebrahim Taghinezhad; Antoni Szumny; Mohammad Kaveh; Vali Rasooli Sharabiani; Anil Kumar; Naoto Shimizu. Parboiled Paddy Drying with Different Dryers: Thermodynamic and Quality Properties, Mathematical Modeling Using ANNs Assessment. Foods 2020, 9, 86 .

AMA Style

Ebrahim Taghinezhad, Antoni Szumny, Mohammad Kaveh, Vali Rasooli Sharabiani, Anil Kumar, Naoto Shimizu. Parboiled Paddy Drying with Different Dryers: Thermodynamic and Quality Properties, Mathematical Modeling Using ANNs Assessment. Foods. 2020; 9 (1):86.

Chicago/Turabian Style

Ebrahim Taghinezhad; Antoni Szumny; Mohammad Kaveh; Vali Rasooli Sharabiani; Anil Kumar; Naoto Shimizu. 2020. "Parboiled Paddy Drying with Different Dryers: Thermodynamic and Quality Properties, Mathematical Modeling Using ANNs Assessment." Foods 9, no. 1: 86.

Original article
Published: 03 January 2020 in Journal of Food Process Engineering
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In the study, drying process of quince fruit was accomplished in a microwave‐convective dryer (MCD). The experiments were carried out at microwave power levels of 100, 200, and 300 W, air temperatures of 40, 55, and 70°C, and air velocities of 0.5, 1, and 1.5 m/s. Nevertheless, three artificial intelligence techniques consisted of artificial neural networks (ANNs), particle swarm optimizer (PSO), and grey wolf optimizer (GWO) were evaluated to predict the parameters of Deff, SEC, ΔE, and Sb. In the evaluation the data by ANNs, input parameters of networks consisted the values of air temperature, microwave power, and air velocity. According to the results, the maximum values of effective moisture diffusivity ( Deff) and specific energy consumption (SEC) were 1.71 × 10−9 m2/s and 126.07 kWh/kg, respectively. In addition, minimum values of total change in color (ΔE) and shrinkage ( Sb) of quince achieved 10.85 and 33.85%, respectively. For predicting all parameters, three models used in the study represented good predictive capability with R2 > 0.97. The obtained results showed that the GWO model had better predictive performance than the ANN and PSO models. Practical Application Drying food and agricultural products by application of microwave‐hot air blend dryers can be a good alternative to hot air and microwave dryers. Microwave energy infiltrates the product and facilitates heat release from the product and thus reduces drying time compared to single dryers. The main aim of applying such different models, mathematical simulation or modeling in the drying technology of agricultural products is to transform physical qualities and their interactions into numerical quantities and mathematical relationships.

ACS Style

Ebrahim Taghinezhad; Mohammad Kaveh; Ahmad Jahanbakhshi; Iman Golpour. Use of artificial intelligence for the estimation of effective moisture diffusivity, specific energy consumption, color and shrinkage in quince drying. Journal of Food Process Engineering 2020, 43, 1 .

AMA Style

Ebrahim Taghinezhad, Mohammad Kaveh, Ahmad Jahanbakhshi, Iman Golpour. Use of artificial intelligence for the estimation of effective moisture diffusivity, specific energy consumption, color and shrinkage in quince drying. Journal of Food Process Engineering. 2020; 43 (4):1.

Chicago/Turabian Style

Ebrahim Taghinezhad; Mohammad Kaveh; Ahmad Jahanbakhshi; Iman Golpour. 2020. "Use of artificial intelligence for the estimation of effective moisture diffusivity, specific energy consumption, color and shrinkage in quince drying." Journal of Food Process Engineering 43, no. 4: 1.

Journal article
Published: 13 December 2019 in Scientia Horticulturae
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The use of nanoparticles to transfer genes to plant cells can solve some problems encountered in other gene transfer methods, including limited host range in the use of Agrobacterium, cell wall removal in the use of polyethylene glycol and electroporation, and high cell damage while using a gene gun. In this research, cationic carbon nanotubes (CNTs) were used to transfer ssDNA-FITC to German chamomile cells. The ability of nanoparticles to interact with and protect of DNA against enzymes and ultrasound damages was investigated using 0.8 % agarose gel. To investigate the morphology of CNTs loaded with DNA (Nanotube- Polyethyleneimine/DNA nanoparticles), scanning electron microscopy (SEM) was used. The biocompatibility effect of CNTs (Nanotube- Polyethyleneimine) on German chamomile cells was also determined by trypan blue staining. Agarose gel images showed that CNTs have a high ability to interact with DNA and can effectively protect it from damage by ultrasound and digestive enzymes. In addition, the SEM images of CNTs/DNA nanoparticles showed that these nanoparticles were rod-shaped with lengths around 100–200 nm. The fluorescence microscope results from German chamomile cells treated with CTNs/ssDNA-FITC nanoparticles showed the ability of these nanoparticles to transfer ssDNA-FITC to German chamomile cells. The results also revealed that the simultaneous use of ultrasound and CNTs significantly increased the transfer efficiency of ssDNA-FITC.

ACS Style

Ali Babaei Ghaghelestany; Ahmad Jahanbakhshi; Ebrahim Taghinezhad. Gene transfer to German chamomile (L chamomilla M) using cationic carbon nanotubes. Scientia Horticulturae 2019, 263, 109106 .

AMA Style

Ali Babaei Ghaghelestany, Ahmad Jahanbakhshi, Ebrahim Taghinezhad. Gene transfer to German chamomile (L chamomilla M) using cationic carbon nanotubes. Scientia Horticulturae. 2019; 263 ():109106.

Chicago/Turabian Style

Ali Babaei Ghaghelestany; Ahmad Jahanbakhshi; Ebrahim Taghinezhad. 2019. "Gene transfer to German chamomile (L chamomilla M) using cationic carbon nanotubes." Scientia Horticulturae 263, no. : 109106.

Original article
Published: 08 November 2018 in Journal of Food Process Engineering
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In this article, the effects of air temperature and velocity on drying characteristics of tarragon (Artemisia dracunculus L.) were investigated. The experiments were done at four temperatures of 40, 50, 60, and 70 °C and three air velocities of 1, 1.5, and 2 m/s. According to the results, the drying term of tarragon reduced significantly with increasing drying air temperature. The values of effective moisture diffusivity (Deff) were ranged between 1.34 × 10−10 and 2.74 × 10−10 m2/s. Also, by increasing drying air temperature the values of specific energy consumption (SEC) were decreased. The values of SEC were between 20.50 and 66.90 MJ/kg. Also Deff and SEC values were modeled by particle swarm optimizer (PSO) and gray wolf optimizer (GWO) algorithms. Drying air velocity and air temperature were considered as input parameters for the models. Based on three statistical parameters includes R2, MSE, and MAE for predicting Deff and SEC, GWO performance was better than the PSO. Practical applications The purpose of drying crops is decreasing of moisture content for providing secure storage and significant influence on the quality of dried crops. In this research, the effective moisture diffusivity (Deff) and specific energy consumption (SEC) values were modeled by PSO and GWO algorithms. The procedure can be applied in postharvest processing of medicinal plants.

ACS Style

Hamed Karami; Mohammad Kaveh; Esmaeil Mirzaee‐Ghaleh; Ebrahim Taghinezhad. Using PSO and GWO techniques for prediction some drying properties of tarragon ( Artemisia dracunculus L.). Journal of Food Process Engineering 2018, 41, e12921 .

AMA Style

Hamed Karami, Mohammad Kaveh, Esmaeil Mirzaee‐Ghaleh, Ebrahim Taghinezhad. Using PSO and GWO techniques for prediction some drying properties of tarragon ( Artemisia dracunculus L.). Journal of Food Process Engineering. 2018; 41 (8):e12921.

Chicago/Turabian Style

Hamed Karami; Mohammad Kaveh; Esmaeil Mirzaee‐Ghaleh; Ebrahim Taghinezhad. 2018. "Using PSO and GWO techniques for prediction some drying properties of tarragon ( Artemisia dracunculus L.)." Journal of Food Process Engineering 41, no. 8: e12921.

Original article
Published: 17 October 2018 in Engineering with Computers
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In this paper, multi-stage continuous belt (MSCB) dryer was used for carrot slices drying. Experiments were performed at three air speeds (1, 1.5, and 2 m/s) three belt linear velocities (2.5, 6.5, and 10.5 mm/s), and three air temperatures (40, 55, and 70 °C) in triplicate. Three intelligent systems including Ant-Lion-Optimizer (ALO), Grey-Wolf-Optimizer (GWO) and Whale-Optimization-Algorithm (WOA) models were developed to predict the thermodynamic properties of carrot slices including of effective moisture diffusivity (Deff) and specific energy consumption (SEC). The results revealed that Deff and SEC values were in the range of 1.77–2.90 × 10−9 m2/s and 169.77–551.19 MJ/kg, respectively. The models of ALO, GWO, and WOA were able to predict the value of Deff and SEC. The amounts of correlation coefficient (\(R\)), root-mean-square error (\({\text{RMSE}}\)), and mean absolute error (\({\text{MAE}}\)) for ALO, GWO, and WOA models for predication Deff were obtained (0.9989, 7.81 × 10−12, and 1.50 × 10−12), (0.9993, 5.39 × 10−12, and 1.03 × 10−12) and (0.9994, 4.95 × 10−12, and 9.54 × 10−13), respectively. In addition, The amounts of \(R\), \({\text{RMSE}}\), and \({\text{MAE}}\) for ALO, GWO, and WOA model for predication SEC were obtained (0.9983, 0.6700, and 0.1289), (0.9988, 0.5274, and 0.0715) and (0.9996, 0.2566, and 0.0060), respectively. Therefore, model of WOA can be used to easily and accurately predict Deff and SEC values.

ACS Style

Mohammad Kaveh; Reza Amiri Chayjan; Ebrahim Taghinezhad; Yousef Abbaspour Gilandeh; Abdollah Younesi; Vali Rasooli Sharabiani. Modeling of thermodynamic properties of carrot product using ALO, GWO, and WOA algorithms under multi-stage semi-industrial continuous belt dryer. Engineering with Computers 2018, 35, 1045 -1058.

AMA Style

Mohammad Kaveh, Reza Amiri Chayjan, Ebrahim Taghinezhad, Yousef Abbaspour Gilandeh, Abdollah Younesi, Vali Rasooli Sharabiani. Modeling of thermodynamic properties of carrot product using ALO, GWO, and WOA algorithms under multi-stage semi-industrial continuous belt dryer. Engineering with Computers. 2018; 35 (3):1045-1058.

Chicago/Turabian Style

Mohammad Kaveh; Reza Amiri Chayjan; Ebrahim Taghinezhad; Yousef Abbaspour Gilandeh; Abdollah Younesi; Vali Rasooli Sharabiani. 2018. "Modeling of thermodynamic properties of carrot product using ALO, GWO, and WOA algorithms under multi-stage semi-industrial continuous belt dryer." Engineering with Computers 35, no. 3: 1045-1058.

Original article
Published: 19 September 2018 in Journal of Food Process Engineering
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In this study, different drying conditions were investigated on quality and thermodynamic properties of almond kernel. Experiments were performed using a convection dryer with ultrasound pretreatment in 40, 50, 60, and 70 °C air temperature, 1 m/s air velocity, and duration of ultrasonic pre‐treatment of 0 min (for control sample), 10, 20, and 40 min. The drying kinetic of the almond kernel was estimated by 15 mathematical models. Furthermore, Artificial Neural Networks (ANNs) and Adaptive Neuro‐Fuzzy Inference Systems (ANFIS) were applied to fit the experimental data on the thin layer drying. The lowest and highest values of the effective moisture diffusivity (Deff) was 1.81 × 10−9 and 9.70 × 10−9 m2/s, respectively. Activation energy (Ea) of the samples was obtained between 26.35 and 36.44 kJ/mol. The highest and lowest values of specific energy consumption (SEC) were calculated 561.72 and 169.88 kW hr/kg, respectively. Maximum (13.14%) and the minimum (7.1%) values of shrinkage were achieved at air temperatures of 70 and 40 °C, respectively. The color changing of dried samples was obtained between 9.14 and 17.96. Furthermore, results revealed that the ANFIS model had the high ability to predict the moisture ratio (R2 = 0.9998 and MSE = 0.0003) during drying. As a result, ANFIS model has the highest ability to evaluate all output as compared with other models and ANNs method. Practical applications Algorithms are modern methods that have been successfully applied to solve the various problems and modeling in engineering and science. Drying is one of the oldest procedures to preserve the food quality. Reduction of moisture content to a certain value can be caused to decay and minimize the microbiological activity and deteriorating chemical reactions in agricultural products, respectively. Determination of almond drying process under convective with ultrasound pre‐treatment dryer in terms of desirable thermal properties (effective moisture diffusivity and energy consumption) provides the high‐quality products. Furthermore, this research can be able to provide a technical basis for almond drying and the related equipment designing.

ACS Style

Mohammad Kaveh; Ahmad Jahanbakhshi; Yousef Abbaspour-Gilandeh; Ebrahim Taghinezhad; Mohammad Bagher Farshbaf Moghimi. The effect of ultrasound pre-treatment on quality, drying, and thermodynamic attributes of almond kernel under convective dryer using ANNs and ANFIS network. Journal of Food Process Engineering 2018, 41, e12868 .

AMA Style

Mohammad Kaveh, Ahmad Jahanbakhshi, Yousef Abbaspour-Gilandeh, Ebrahim Taghinezhad, Mohammad Bagher Farshbaf Moghimi. The effect of ultrasound pre-treatment on quality, drying, and thermodynamic attributes of almond kernel under convective dryer using ANNs and ANFIS network. Journal of Food Process Engineering. 2018; 41 (7):e12868.

Chicago/Turabian Style

Mohammad Kaveh; Ahmad Jahanbakhshi; Yousef Abbaspour-Gilandeh; Ebrahim Taghinezhad; Mohammad Bagher Farshbaf Moghimi. 2018. "The effect of ultrasound pre-treatment on quality, drying, and thermodynamic attributes of almond kernel under convective dryer using ANNs and ANFIS network." Journal of Food Process Engineering 41, no. 7: e12868.

Journal article
Published: 01 September 2018 in Information Processing in Agriculture
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ACS Style

Mohammad Kaveh; Vali Rasooli Sharabiani; Reza Amiri Chayjan; Ebrahim Taghinezhad; Yousef Abbaspour-Gilandeh; Iman Golpour. ANFIS and ANNs model for prediction of moisture diffusivity and specific energy consumption potato, garlic and cantaloupe drying under convective hot air dryer. Information Processing in Agriculture 2018, 5, 372 -387.

AMA Style

Mohammad Kaveh, Vali Rasooli Sharabiani, Reza Amiri Chayjan, Ebrahim Taghinezhad, Yousef Abbaspour-Gilandeh, Iman Golpour. ANFIS and ANNs model for prediction of moisture diffusivity and specific energy consumption potato, garlic and cantaloupe drying under convective hot air dryer. Information Processing in Agriculture. 2018; 5 (3):372-387.

Chicago/Turabian Style

Mohammad Kaveh; Vali Rasooli Sharabiani; Reza Amiri Chayjan; Ebrahim Taghinezhad; Yousef Abbaspour-Gilandeh; Iman Golpour. 2018. "ANFIS and ANNs model for prediction of moisture diffusivity and specific energy consumption potato, garlic and cantaloupe drying under convective hot air dryer." Information Processing in Agriculture 5, no. 3: 372-387.

Original
Published: 15 May 2018 in Heat and Mass Transfer
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The thin-layer convective- infrared drying behavior of white mulberry was experimentally studied at infrared power levels of 500, 1000 and 1500 W, drying air temperatures of 40, 55 and 70 °C and inlet drying air speeds of 0.4, 1 and 1.6 m/s. Drying rate raised with the rise of infrared power levels at a distinct air temperature and velocity and thus decreased the drying time. Five mathematical models describing thin-layer drying have been fitted to the drying data. Midlli et al. model could satisfactorily describe the convective-infrared drying of white mulberry fruit with the values of the correlation coefficient (R2=0.9986) and root mean square error of (RMSE= 0.04795). Artificial neural network (ANN) and fuzzy logic methods was desirably utilized for modeling output parameters (moisture ratio (MR)) regarding input parameters. Results showed that output parameters were more accurately predicted by fuzzy model than by the ANN and mathematical models. Correlation coefficient (R2) and RMSE generated by the fuzzy model (respectively 0.9996 and 0.01095) were higher than referred values for the ANN model (0.9990 and 0.01988 respectively).

ACS Style

Shahpour Jahedi Rad; Mohammad Hossein Kaveh; Vali Rasooli Sharabiani; Ebrahim Taghinezhad. Fuzzy logic, artificial neural network and mathematical model for prediction of white mulberry drying kinetics. Heat and Mass Transfer 2018, 54, 3361 -3374.

AMA Style

Shahpour Jahedi Rad, Mohammad Hossein Kaveh, Vali Rasooli Sharabiani, Ebrahim Taghinezhad. Fuzzy logic, artificial neural network and mathematical model for prediction of white mulberry drying kinetics. Heat and Mass Transfer. 2018; 54 (11):3361-3374.

Chicago/Turabian Style

Shahpour Jahedi Rad; Mohammad Hossein Kaveh; Vali Rasooli Sharabiani; Ebrahim Taghinezhad. 2018. "Fuzzy logic, artificial neural network and mathematical model for prediction of white mulberry drying kinetics." Heat and Mass Transfer 54, no. 11: 3361-3374.