Modelling some engineering properties of walnut kernel undergoing different drying methods with microwave pre-treatment

J Food Process Engineering

et al. 2020

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.

Section: Resultssupporting

confidence: 83%

Use of artificial intelligence for the estimation of effective moisture diffusivity, specific energy consumption, color and shrinkage in quince drying

Taghinezhad

J Food Process Engineering

et al. 2020

“…Almonds with the same degree of maturity were bought from a local market in Ardabil. In order to preserving of product MC, the samples were kept in the refrigerator at the temperature of 4 ± 1 °C and were transferred from refrigerator to laboratory 2 hr before conducting of experiments (Jafarifar, Chayjan, Dibagar, & Alaei, ). The standard method of using an oven's hot air (Memmert UNE 500 model) was applied for determination of MC.…”

Section: Methodsmentioning

confidence: 99%

The effect of ultrasound pre‐treatment on quality, drying, and thermodynamic attributes of almond kernel under convective dryer using ANNs and ANFIS network

J Food Process Engineering

Abbaspour‐Gilandeh

et al. 2018

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.

“…The amounts of

R^{2}

and

italicMSE

are shown in Table 3 for all of the models. According to the results, Midilli et al model was selected as the best model for prediction of pistachio MR. Jafarifar, Amiri Chayjan, Dibagar, and Alaei (2017) and Kaveh, Jahanbakhshi, et al (2018) observed that model of Midilli was the best model in order to predict the thin layer drying behavior of walnut (in infrared‐vacuum, continuous, and infrared‐fluidized bed) and almond kernel (in the convective dryer with US pretreatment).…”

Section: Resultsmentioning

confidence: 99%

Assessment of kinetics, effective moisture diffusivity, specific energy consumption, shrinkage, and color in the pistachio kernel drying process in microwave drying with ultrasonic pretreatment

Taghinezhad

et al. 2020

J Food Process Preserv

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 (Deff) were 1.43 × 10–8 and 4.30 × 10–8 m2/s, respectively. Specific energy consumption (SECtotal) 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 (R2 = .9997) and Mean Square Error (b∗ = 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.