Using PSO and GWO techniques for prediction some drying properties of tarragon (<scp><i>Artemisia dracunculus</i></scp> L.)

Karami, Hamed; Kaveh, Mohammad; Mirzaee‐Ghaleh, Esmaeil; Taghinezhad, Ebrahim

doi:10.1111/jfpe.12921

Cited by 17 publications

(11 citation statements)

References 42 publications

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“…Similar results were obtained by Karami, Kaveh, Mirzaee‐Ghaleh, and Taghinezhad () for predicting the D eff and SEC of tarragon by means of GWO and PSO whereas the results were ( R 2 = 0.9995 and R 2 = 0.9992 for D eff in GWO and PSO, respectively) and ( R 2 = 0.9997 and R 2 = 0.9996 for SEC in GWO and PSO, respectively) in a hybrid dryer.…”

Section: Resultssupporting

confidence: 85%

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

Taghinezhad

Kaveh

Jahanbakhshi

et al. 2020

J 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.

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Section: Resultssupporting

confidence: 85%

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

Taghinezhad

Kaveh

Jahanbakhshi

et al. 2020

J Food Process Engineering

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“…In principal components regression, we use PCA to decompose the independent ( x ) variables into an orthogonal basis and select a subset of those components as the variables to predict ( y ). The model performance can be evaluated by R 2 and RMSE (Karami, Kaveh, Mirzaee‐Ghaleh, & Taghinezhad, 2018; Kaveh, Karami, & Jahanbakhshi, 2020):

R^{2} = 1 - \frac{\sum_{i = 1}^{N} {(S_{k} - T_{k})}^{2}}{\sum_{i = 1}^{N} {(S_{k} - T_{m})}^{2}}

R M S E = \frac{1}{N} {false\sum}_{i = 1}^{N} |S_{k} - T_{k}|

…”

Section: Methodsmentioning

confidence: 99%

Application of the E‐nose machine system to detect adulterations in mixed edible oils using chemometrics methods

Karami

Rasekh

Mirzaee‐Ghaleh

2020

J Food Process Preserv

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Foodstuff adulteration involves addition of any low‐cost substances to the high‐price materials to reduce the content of the expensive components, and hence decrease the production cost and reach to the maximum profit. An electronic nose was used in this study to detect the adulterations in mixed edible oils. The acidity, peroxide, anisidine, and Totox values of the edible oil samples were measured according to the official American Oil Chemist Society (AOCS) standard. The results were analyzed by Cluster analysis (CA), principle component analysis (PCA), principal component regression (PCR), linear discriminant analysis (LDA), and artificial neural network (ANN) methods with accuracy of 95, 98, 98, 88, and 97.3%, respectively. According to the results, the ANN method with structure of 8‐7‐5 showed the highest accuracy in classification of oil adulteration. Its correct classification ratio, mean square errors, and correlation (r) were 97.3%, .117211, and .0963, respectively. The results also indicated that the proposed method can be used as an alternative of the official AOCS methods to innovatively detect the edible oil adulteration with high accuracy and speed. Practical applications Lipid oxidation is one of the major causes of food spoilage especially in those containing oil. AOCS has developed various methods to evaluate the oxidation status of the oil assets. However, these chemical tests are time‐consuming, destructive, and costly and require several glassware and reagents. E‐nose could be used for real‐time monitoring of the volatile components of the food to evaluate different features of the product. Generally, E‐nose evaluates mixture of smells released form a sample and is a reliable, nondestructive, cost‐effective, and portable method with high feasibility and speed as well as simple use. CA, PCA, and ANN methods were also applied for qualitative differentiation of different adulteration percentages in oxidized and nonoxidized oils.

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“…Rosemary (Rosmarinus officinalis L.) plants were harvested. The initial moisture determination of samples was performed using the oven method at 70 • C for 24 (h) until a constant weight was achieved [6]. As a result, the fresh rosemary samples had an initial moisture content of 81% (% d.b.…”

Section: Sample Preparationmentioning

confidence: 99%

“…In addition to the low drying rate, this method is subject to several kinds of interference, such as insects, wind, or dust. Several drying methods have been developed to address these problems, including solar dryers, which represent an inexpensive method that can be applied in many places without requiring a secondary power source [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Thermodynamic Evaluation of the Forced Convective Hybrid-Solar Dryer during Drying Process of Rosemary (Rosmarinus officinalis L.) Leaves

et al. 2021

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This study aimed to examine the energy and exergy indices of the rosemary drying process in a hybrid-solar dryer (HSD) and the effects of air-drying parameters on these thermodynamic indices. Drying experiments were carried out at four levels of air temperature (40, 50, 60, and 70 ∘C) and three levels of air velocity (1, 1.5, and 2 m/s). Energy and exergy were calculated by application of the first and second laws of thermodynamics. Based on the principal laws, energy efficiency, exergy losses, and exergetic improvement potential rate, were evaluated. The results showed that the energy utilization ratio (EUR) ranged from 0.246 to 0.502, and energy utilization (EU) ranged from 0.017 to 0.060 (kJ/s). Exergy loss and efficiency varied from 0.009 to 0.028 (kJ/s) and from 35.08% to 78.5%, respectively, and increased with increased temperature and air velocity. It was found that the exergy loss rate was affected by temperature and air velocity because the overall heat transfer coefficient was different under these conditions. By comparison, with increasing temperature and air velocity, the exergy efficiency increased. Because most energy is used to evaporate moisture, this behavior may be explained by improved energy utilization. The drying chamber sustainability index ranged from 0.0129 to 0.0293. This study provides insights into the optimization process of drying operations and operational parameters in solar hybrid dryers that reduce energy losses and consumption.

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Using PSO and GWO techniques for prediction some drying properties of tarragon (Artemisia dracunculus L.)

Cited by 17 publications

References 42 publications

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

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

Application of the E‐nose machine system to detect adulterations in mixed edible oils using chemometrics methods

Thermodynamic Evaluation of the Forced Convective Hybrid-Solar Dryer during Drying Process of Rosemary (Rosmarinus officinalis L.) Leaves

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