Modeling Drying Properties of Pistachio Nuts, Squash and Cantaloupe Seeds under Fixed and Fluidized Bed Using Data-Driven Models and Artificial Neural Networks

Kaveh, Mohammad; Chayjan, Reza Amiri; Khezri, Behrooz

doi:10.1515/ijfe-2017-0248

Cited by 17 publications

(11 citation statements)

References 64 publications

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“…This structure had 0.2961, 0.3179, and 0.3614 of RMSE and 0.9981, 0.9935, and 0.9892 of R for train, validation, and test, respectively. In more researches (Abbaszadeh, Motevali, Khoshtaghaza, & Kazemi, ; Kaveh, Chayjan, & Khezri, ; Movagharnejad & Nikzad, ; Omid, Baharlooei, & Ahmadi, ) drying conditions and time were be used as input in the ANN for modeling and predicting of the moisture content like the static model of this study. But this model did not have good ability for online drying control especially in microwave drying process.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, the lowest color deterioration was found 10.10 for ΔE and 42.32 for BI with highest air temperature of 70 C and lowest MPD of 1.5 W/g with constantan status. Finally, with concerning to the Table 3 and (Abbaszadeh, Motevali, Khoshtaghaza, & Kazemi, 2011;Kaveh, Chayjan, & Khezri, 2018;Movagharnejad & Nikzad, 2007;Omid, Baharlooei, & Ahmadi, 2009) drying conditions and time were be used as input in the ANN for modeling and predicting of the moisture content like the static model of this study. But this model did not have good ability for online drying control especially in microwave drying process.…”

Section: Resultsmentioning

confidence: 99%

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Drying kinetic and artificial neural network modeling of mushroom drying process in microwave‐hot air dryer

Amin

Behroozi‐Khazaei

Sharifian

2018

J Food Process Engineering

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Modeling of moisture content variation under variable microwave power in microwave‐hot air dryer is challenging. In this study, one static and one dynamic of ANN were investigated for modeling of whole mushroom drying process. The experiments were done in three levels of hot air temperature (23, 50, and 70 °C), three levels of microwave power density (MPD) (1.5, 2, and 2.5 W/g) and two statuses of microwave power during drying process (constant and variable). The results demonstrated that the two distinct falling rate periods were observed at low MPD with any air temperatures and power density status. The lowest specific energy consumption was found in 23 °C and 2.5 fixed MPD and lowest color deterioration found in 70 °C and 1.5 fixed MPD. The results also showed the 3–6–7–1 structure of dynamic model with 0.2179 RMSE and 0.9914 R values had better results than other structures and would be very suitable in a predictive control system for drying control. Practical applications Volumetric and high efficiency heating are the primary benefit of microwave drying and lot of industries and producers interest to use it. Also, modeling of drying kinetics and evaluating of product quality in each drying method provide valuable information to improve their drawbacks and recommend it for industry. Application of microwave technology in dehydration of mushroom can result in significant improvement in quality and energy consumption. On the other hand investigating a robust ANN model for predicting the moisture content helps to better prediction and control of microwave‐hot air dryer in industrial scale.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Drying kinetic and artificial neural network modeling of mushroom drying process in microwave‐hot air dryer

Amin

Behroozi‐Khazaei

Sharifian

2018

J Food Process Engineering

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“…Depending on the states (original and fake oils), the output layer indicates the prediction (desired) values. The performance of the designed networks was evaluated by mean square error (MSE) and correlation coefficient ( r ) (Kaveh, Chayjan, & Khezri, 2018). To train the network, the number of neurons in the hidden layer and MSE were changed.…”

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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“…At this stage, the radius of the walnut kernel ( r e ) is considered constant. By taking logarithms from the sides of Equation (6), can be obtaining a linear equation (Kaveh, Amiri Chayjan, & Khezri, ):

\ln false(M R false) = \ln (\frac{6}{π^{2}}) - (\frac{D_{eff} π^{2} t}{r_{e}^{2}})

…”

Section: Methodsmentioning

confidence: 99%

The effect of microwave and convective dryer with ultrasound pre‐treatment on drying and quality properties of walnut kernel

Abbaspour‐Gilandeh

Kaveh

Jahanbakhshi

2019

J Food Process Preserv

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In this study, the drying kinetics of walnuts was investigated including specific energy consumption (SEC), shrinkage, and walnut kernels' color in two dryers, namely ultrasonic‐convection (US‐CON) and ultrasonic‐microwave (US‐MIC). To carry out this study, walnut samples were dried at three MIC powers (270, 450, and 630 W), three input air temperatures (40, 55, and 70°C) and four duration of ultrasonic pre‐treatment (0 min (control sample), 10, 20, and 40 min). Fifteen mathematical models were used to describe the walnuts' moisture ratio. The results showed that the Midilli et al. model was the best model for describing the process of drying walnuts in US‐CON and US‐MIC dryers. Effective moisture diffusivity (Deff) values were calculated to be between 2.77 × 10–9 and 5.56 × 10–9 m2/s for the US‐CON dryer and 3.12 × 10–9 to 8.99 × 10–9 m2/s for the US‐MIC dryer. The lowest rates of color change were observed in the US‐MIC dryer at the lowest microwave power and the lowest duration of ultrasonic pre‐treatment. The lowest SEC for the US‐CON dryer was 19.52 kWh/kg, and it was 15.90 kWh/kg for the US‐MIC dryer. The highest rate of shrinkage was 14.21%, which was seen in the US‐CON dryer at the air temperature of 70°C and the duration of ultrasonic pre‐treatment of 40 min. According to the results, the best quality of walnut with the lowest changes of color and shrinkage in the microwave dryer was calculated at microwave power 270 W and control sample. Practical applications Drying foods and agricultural products using microwave dryers is a relatively cheap method and has attracted the attention of many researchers today. Also, numerous studies have been conducted on drying agricultural products through microwave and convection dryers with ultrasound pre‐treatment. The result of this study could be applied for gathering comprehensive information regarding the drying process (kinetic, Deff, Ea, and SEC) as well as the qualitative properties (color and shrinkage) of walnut kernels dried using the microwave and convective hot air dryers accompanied by ultrasound pre‐treatment.

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Modeling Drying Properties of Pistachio Nuts, Squash and Cantaloupe Seeds under Fixed and Fluidized Bed Using Data-Driven Models and Artificial Neural Networks

Cited by 17 publications

References 64 publications

Drying kinetic and artificial neural network modeling of mushroom drying process in microwave‐hot air dryer

Drying kinetic and artificial neural network modeling of mushroom drying process in microwave‐hot air dryer

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

The effect of microwave and convective dryer with ultrasound pre‐treatment on drying and quality properties of walnut kernel

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