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Coffee is one of the most popular and frequently consumed beverages on the planet. Coffee has a significant commercial value, estimated to be in the billions of dollars and consumption has risen steadily over the last two decades. Near-infrared spectroscopy is one of the non-destructive optical technologies for the evaluation of agricultural products to identify food adulteration. Thus, it is an interesting and worthwhile subject to research and study. In this research, a near-infrared spectroscopy approach along with statistical methods of principal component analysis (PCA), partial-least-squares regression (PLSR), latent dirichlet allocation (LDA), and artificial neural network (ANN) as a fast and non-destructive method was used with to detect and classify coffee beans using reference data obtained by gas chromatography–mass spectrometry (GC–MS). Results showed that the accuracy of PLSR, LDA, and ANN while our reference data was palmitic acid, respectively were 97.3%, 97.92%, and 97.3% and while reference data was caffeine, accuracy results were 94.71%, 95.83%, and 98.96%, respectively.

Although the application of synthetic chemicals is the main method in the management of insect pests, their overuse has led to public concerns about environmental pollution, threats to human health, and acute and chronic toxicity on non-target organisms. Plant essential oils have introduced as healthy, available, and effective alternatives to detrimental chemicals in recent years. Further, it is necessary to predict the exact amount of required pesticide to save costs and determine the optimal conditions for achievement to the best outcomes. Accordingly, the toxicity of Eucalyptus globulus Labill essential oil against the adults of a cosmopolitan pest Tribolium castaneum Herbst (red flour beetle) along with its modeling and optimization was assessed using Response Surface Methodology (RSM). The coefficients of the essential oil concentration and time as independent variables are positive, showing their increase results in the augmentation of insect pest mortality. E. globulus essential oil showed prospective concentration-time dependent fumigant toxicity against T. castaneum. A quadratic polynomial equation was achieved for the toxicity of E. globulus essential oil using multiple regression analysis: 7.33413 + 0.20191A + 0.47313B + 4.64054E-003AB + 0.016349B2, in which A and B are the exposure time and essential oil concentration. The accuracy of the introduced model was approved through the analysis of variance. Results of the optimization indicated that 45.50 μl/l of essential oil and 72.00 h-exposure time would be adequate to achieve 92.45% mortality of T. castaneum. According to the results of current study, E. globulus essential oil has high potential in the management of T. castaneum and the Response Surface Methodology (RSM) is a suitable method to the optimization and modelling of this bio-effect.

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 R 2 cal ≥ .85 and R 2 pre ≥ .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 R 2 cal ≥ .88 and R 2 pre ≥ .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., Determination coefficients of calibration models to flour samples of AC, PC, BDV, and SBV generated by the PLS regression were obtained as R 2 cal ≥ .85 and R 2 pre ≥ .80. Root mean square error of calibration (RMSEC) was calculated as 0.393, 0.07, 2.55, and 1.33, respectively, and to grain samples, was obtained as R 2 cal ≥ .88 and R 2 pre ≥ .71 for calibration and prediction. RMSEC was measured as 0.303, 0.27, 2.59, and 3.11, respectively.

The utilization of natural materials in the post-harvest process of agricultural products is necessary for the production of safe food. In recent years, the use of essential oil extracted from aromatic plants has shown significant potential for insect pest management. Toxicity and antifeedant effects of essential oil isolated from aerial parts of Teucrium polium L. have been investigated against the red flour beetle, Tribolium castaneum Herbst, as one of the most detrimental insect pests of post-harvest cereals in the present study. The chemical profile of this oil was also assessed by gas chromatography-mass spectrometry (GC-MS) and lycopersene (26.00%), dodecane (14.78%), 1,5-dimethyl decahydronaphthalene (9.27%) and undecane (7.18%) were identified as main components. For evaluation of the fumigant toxicity and antifeedant effects using multiple regression analysis, a quadratic polynomial and linear equation were obtained, respectively. Adequacy and accuracy of the fitted models were checked through analysis of variance. T. polium essential oil exhibited significant fumigant toxicity on the T. castaneum adults and based on modeling using RSM, the concentration of 20 µl/l and 72 min exposure time was calculated as the optimum conditions for 97.97% mortality with 87.8% desirability. A concentration of 14.13 µl/l was also estimated as the optimum value for 94.66% Feeding Deferens Index with 92% desirability. The mortality and anti-nutritional effect, in general, increased with increasing of exposure times and the essential oil concentrations. Results designated a great potential of T. polium essential oil for management of the red flour beetle. Further, it was found that the Response Surface Methodology was a promising method for the prediction of these bio-effects.

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...

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.

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.

In Iran, more than 30% of agricultural products turn into waste at different stages from harvesting to consumption. Thus, main factors for performing of this present study are including of: (a) the importance of tomato as an agricultural product and (b) lack of information about reducing waste during tomato processing. In this study, some physical, nutritional, mechanical, and hydrodynamic properties of tomato were measured under standard conditions. Physical properties included the length, width, thickness, mean diameter (geometric and arithmetic), mass, volume, density, sphericity, surface area, and aspect ratio. Also, nutritional properties, moisture, dry matter, pH, total soluble solid (TSS), and titration acidity (TA) of tomato were evaluated. The mechanical properties of tomato (compression and shear) were measured using Instron instrument. The hydrodynamic properties were measured with water in transportation, separation, and sorting of tomatoes. The physical properties were including of length, width, thickness, mass, volume, and geometric and arithmetic mean diameters showed a direct relationship with the size of tomatoes. Also, volumetric mass (density) had an inverse relation with tomato size. Yield point and shear force were obtained 51.27 and 22.20 N, respectively. The nutritional properties such as pH value, TSS, and TA were equal to 4.22, 22.23οBrix, and 2%, respectively. The hydrodynamic properties of tomatoes such as the terminal velocity, the tomatoes' rise time in the water column, the buoyancy force, and the drag force were obtained to be equal to 0.05 m/s, 10.11 S, 0.52 N, and 0.17 N, respectively.

In this research, the effects of intermittent drying variables on some quality properties of parboiled rice were investigated, then a mathematical model was applied to predict the value of quality features non-destructively using response surface methodology (RSM). The intermittent drying variables consisted of hot air temperature (40, 50 and 60?C), radiation intensity (0.21, 0.31 and 0.41 W/cm2) and microwave power (100, 200 and 300 W). The intermittent drying was performed at two stages with a tempering time between drying steps using a hybrid drying of hot air?infrared radiation and microwave drying at the first stage and second stage, respectively. According to RSM results, the effect of drying variables on the quality properties of parboiled rice were significant (p < 0.01). Also, the best mathematical model for prediction of quality properties of samples was the quadratic equation (R2 = 0.96-0.98). The HRY (61.8-73.2%), hardness (118.63-215.27 N) and color value (17.28-19.22) increased, while the lightness (64.17-59.51) decreased during drying. RSM can be able to predict the optimization parameters for the best quality properties (i.e., HRY = 72.42%, lightness = 59.47, color value = 19.25 and hardness = 213.91 N) based on temperature of 60?C, radiation of 0.41 W/cm2 and power of 300 W.

Infrared assisted vacuum drying technology is a newly emerged strategy, which found its place in the food drying industry. Therefore, in this research, the effectiveness of near infrared (NIR) and ...

The main purpose of this study was to develop and apply an adaptive neuro-fuzzy inference system (ANFIS) and Artificial Neural Networks (ANNs) model for predicting the drying characteristics of potato, garlic and cantaloupe at convective hot air dryer. Drying experiments were conducted at the air temperatures of 40, 50, 60 and 70 °C and the air speeds of 0.5, 1 and l.5 m/s. Drying properties were including kinetic drying, effective moisture diffusivity (Deff) and specific energy consumption (SEC). The highest value of Deff obtained 9.76 × 10−9, 0.13 × 10−9 and 9.97 × 10−10 m2/s for potato, garlic, and cantaloupe, respectively. The lowest value of SEC for potato, garlic, and cantaloupe were calculated 1.94 × 105, 4.52 × 105 and 2.12 × 105 kJ/kg, respectively. Results revealed that the ANFIS model had the high ability to predict the Deff (R2 = 0.9900), SEC (R2 = 0.9917), moisture ratio (R2 = 0.9974) and drying rate (R2 = 0.9901) during drying. So ANFIS method had the high ability to evaluate all output as compared to ANNs method.

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).

This research was investigated to the thin-layer drying of terebinth fruit under convective infrared microwave (CIM) conditions with initial moisture content about 4.28% (g water/g dry matter). The effects of drying different conditions were studied on the effective moisture diffusivity, activation energy, specific energy, shrinkage, color, and mechanical properties of terebinth. Experiments were conducted at three air temperatures (45, 60, and 70 °C), three infrared power (500, 1000, and 1500 W) and three microwave power (270, 450 and 630 W). All these experiments were carried out under air velocity of 0.9 m/s. The effective moisture diffusivity of terebinth was obtained as 1.79 × 10−9 to 15.77 × 10−9 m2/s during drying. The activation energy of terebinth samples was measured to be 12.70 to 32.28 kJ/mol. To estimate the drying kinetic of terebinth, seven mathematical models were used to fit the experimental data of thin-layer drying. Results showed that the Midilli et al. model withR2 = 0.9999,χ2 = 0.0001 andRMSE = 0.0099 had the best performance in prediction of moisture content. Specific energy consumption was within the range of 127.62 to 678.90 MJ/kg. The maximum shrinkage during drying was calculated 69.88% at the air temperature 75 °C, infrared power of 1500 W, and microwave power 630 W. Moreover, the maximum values of the ΔL∗ (15.89), Δa∗ (12.28), Δb∗(−0.12), and total color difference (ΔE= 17.44) were calculated in this work. Also, the maximum rupture force and energy for dried terebinth were calculated to be 149.2 N and 2845.4 N.mm, respectively.

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., Hot air drying experiments have been conducted under three different temperatures and four pretreatments, including pretreatments using thermal blanching, pulse, chemical, and ultrasonic vibration. Then, drying kinetics, quality, and energy consumption of blackberry in hot air dryer were studied.

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.

Bu çalışmada, buğdaydaki protein miktarınıtahmin etmek için, tahribatsız ve hızlı bir gözlem yöntemi olan yakınkızılötesi spektroskopi (KS) tekniği kullanılmıştır. Sırasıyla spektralbantları ve en iyi modelleri seçmek için Kısmi En Küçük Kareler Regresyonu(KEKKR) ve Yapay Sinir Ağı (YSA) yöntemleri kullanılmıştır. Modellerinverimliliğini karşılaştırmak için Kök-ortalama-kare hata (KOKH) ve R2uygulanmıştır. Cascade ileri geri yayılımının (CİGY) en iyi sonucu,Levenberg-Marquardt (LM) ile 8-8-1 ağ yapısı ve TANSIG-TANSIG-PURELIN(TANSIG-TANSIG-PURELIN (R𝑀𝑆𝐸 = 0.0289 ve 𝑅2) 'nin işlevi ile ilgilidir. YSA modeli içintahmin sonuçları (𝑅2 = 0.9881), KEKKR modelinden (𝑅2 = 0.9783)daha iyi bulunmuştur. Bu nedenle, sonuçlara göre, buğdaydaki protein miktarınınbelirlenmesinde KS'nin tahmin etme potansiyeli yüksek olduğu söylenebilir., Inthis study to predict amount of protein in wheat, near infrared spectroscopytechnique (NIRS) was used that is a non-destructive and fast observing method. Partial Least Squares Regression (PLSR) andArtificial Neural Network (ANN) methods were used to choose the spectral bandsand the best models, respectively. To compare the efficiency of modelsRoot-mean-square error (RMSE) and R2 were applied. The finestconsequence by cascade forward back propagation (CFBP) was related to networkstructure of 8-8-1 with Levenberg-Marquardt (LM), and function ofTANSIG-TANSIG-PURELIN (TANSIG-TANSIG-PURELIN (R𝑀𝑆𝐸=0.0289 and 𝑅2=0.9881 at 14 epochs). The consequences of estimationfor ANN model (𝑅2=0.9881) was better than the PLSR model (𝑅2=0.9783). Therefore, according to the results, it canbe said that NIRS has a high potential for predicting the amount of protein inwheat.

Iranian paddy (Fajr) is the most popular rice for export and consumption in Iran but it has poor milling yield. To solve this problem, parboiling has been used for improving its milling quality. In this study, the effect of various parboiling conditions (soaking at temperatures of 55–75°C and steaming times for 2–10 min) on some quality properties of Fajr paddy was investigated. After parboiling, the physical properties (degree of milling, head rice yield, lightness and color value) and mechanical properties (rupture force) of parboiled rice were measured. Head rice yield and mean value of rupture force increased significantly (p < 0.05) from 50.10% to 62.11–67.05% and from 108.6 to 128.93–227.30 N, respectively. Also, the color value of parboiled rice increased significantly (p < 0.05) by increasing the length of steaming time. The milling degree of unparboiled rice (17.03%) was significantly (p < 0.05) higher than that of the parboiled rice (15.1–16.9% range). Soaking at 65°C and 4 min steaming time gave the highest values of head rice yield, lightness and rupture force. So, this treatment was found to provide the most desirable quality of Fajr parboiled rice.

This study formulates a mathematical relationship correlating the degree of starch gelatinization (DSG) of rice to the paddy moisture-electrical conductivity (EC) of paddy water during the soaking portion of the parboiling process. DSG values of rice were measured using differential scanning calorimetry (DSC). Paddy was parboiled by soaking at 60, 65, 70 and 75C. At each temperature paddy samples were selected at five different soaking times. Optimum soaking times for 60, 65, 70 and 75C were determined to be 240, 180, 120 and 80 min, respectively. Paddy moisture (22.43–34.93%, wet basis), EC of paddy water (1.63–2.71 mS/cm) and DSG of rice (5.38–36.90%) increased with increasing temperature and soaking time. It was found that a linear relationship exists between DSG and EC, as well as between DSG and moisture content during soaking. EC of paddy water correlates well with DSG of rice during soaking. An online measurement system of paddy water EC was manufactured and evaluated. The relationship between DSG and EC can be utilized for quick and simple determination of DSG during soaking and eliminating the need for conventional chemical analysis. Practical Applications Parboiled rice enjoys high popularity because of its high nutritional content. The soaking stage is the most important stage of the parboiling process. Soaking conditions (temperature and time of soaking) largely determine the characteristics of the parboiled product and can be evaluated by DSG. Therefore, from an industrial point of view, it is important to apply the best processing conditions for parboiling rough rice. Hot soaking requires precise control because starch granules are gelatinized during soaking. Determination of DSG is commonly done by DSC instrument. This method is associated with high costs and cannot provide online data. Online measurement of EC values of paddy water during soaking can be used to easily and accurately predict the final time of the soaking stage. This study was developed as a quick and easy method for online determination of DSG values during soaking. The method presented here can be used for monitoring and controlling the soaking stage by measuring paddy water EC via an online system based on DSG values during soaking.

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.

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.

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.

Although utilization of synthetic chemicals is inevitable for management of economically detrimental agents, numerous side-effects such as environmental contaminations and effects of non-target organisms associated with them. Plant essential oils with low/without toxicity on mammals and as bio-degradable natural materials have been considered for different pests and fungi management in the recent years. In the present study, the essential oil of Thymus kotschyanus isolated by a Clevenger apparatus and its mycelial growth inhibition was measured against two phytopathogenic fungi Botrytis cinerea and Fusarium graminearum . The best models for predicting of antifungal effects were quadratic models. The essential oil showed a prospective mycelial growth inhibition against both phytopathogenic fungi. Optimization of the antifungal effects indicated that 206.207 ppm of the essential oil caused 50% mycelial growth inhibition of B. cinerea after 89.651 h. This value was 85.600 ppm for F. graminearum within 117.194 h. Results of the present study designated a great potential of T. kotschyanus essential oil for management of pathogenic fungi B. cinerea and F. graminearum . Keywords: Essential oil, mycelial growth inhibition, response surface, Thymus kotschyanus

The effects of parboiling indicators (soaking temperature and steaming time) on the degree of starch gelatinization (DSG) and several rice quality properties [head rice yield (HRY), color value, lightness, and hardness] of parboiled rice were investigated. A mathematical model was used to predict DSG and rice quality from the parboiling conditions, which were varied as follows: 60, 65, and 70 °C for the soaking temperature and 2, 6, and 10 min for the steaming time (at 96 °C). A central composite design was applied and a total of 13 experimental runs were generated for each step. An ANOVA test showed the all models were significant (p

The effects of hot air temperature (40, 50 and 60 oC) and Radiation Intensity (RI) (0.21, 0.31 and 0.41 w/cm2) on the response variables (drying time, Head Parboiled Rice Yield (HPRY), color value and hardness)) of parboiled rice were investigated. The drying was performed using hybrid hot air–infrared drying. The optimization of drying variables and the relationship between response variables and the influence factors were analyzed using response surface methodology (RSM). Based on RSM results, the best mathematical model for prediction of HPRY, hardness and color value and drying time of samples was linear(R2= 0.96), quadratic(R2= 0.99), linear(R2= 0.93) and linear(R2= 0.99) equation, respectively. The HPRY (62.13- 68.13%) and hardness (130.27- 247.3 N) increased with increasing drying temperature and RI, while the color value (19.77- 18.03) and drying time (59.72- 34.41 min) decreased. The optimized parameters of drying were obtained 55 oC drying temperature and 0.41 w/ cm2 RI.