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

Jahanbakhshi, Ahmad; Kaveh, Mohammad; Taghinezhad, Ebrahim; Sharabiani, Vali Rasooli

doi:10.1111/jfpp.14449

Cited by 66 publications

(71 citation statements)

References 68 publications

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“…The ultrasound power (UP) was calculated using Equation (18) (Jahanbakhshi et al., 2020; Kaveh et al., 2018).

UP = UI \cos Φ

…”

Section: Methodsmentioning

confidence: 99%

“…where, UP is the ultrasound power (kw), U (V), and I (A) is the applied voltage and current applied to the ultrasound generator, and cos Φ is the power factor considered to be 0.8. The energy consumption for ultrasound device was calculated using Equation (19) (Jahanbakhshi et al., 2020; Kaveh et al., 2018):

{SEC}_{ult} = \frac{UP . t}{m_{vult}}

…”

Section: Methodsmentioning

confidence: 99%

“…where SEC oven is the specific energy consumption for oven dryer (kwh/kg) and W 0 is the primary mass of sample (kg). Using Equation (22), the total specific energy consumption was calculated (Jahanbakhshi et al., 2020; Kaveh et al., 2018):

{SEC}_{total} = {SEC}_{ult} + {SEC}_{oven}

…”

Section: Methodsmentioning

confidence: 99%

“…Equation 22, the total specific energy consumption was calculated (Jahanbakhshi et al, 2020;Kaveh et al, 2018):…”

Section: Specific Energy Consumption Of Hot Air (Oven) Dryermentioning

confidence: 99%

“…Saving energy, increasing the quality of the product, increasing the rate, and reducing the duration of the processes are considered as significant parameters in the application of new technologies. The use of ultrasound waves as a novel pre‐treatment in the process of drying foodstuff has also been taken into account (Abbaspour‐Gilandeh et al, 2019; Jahanbakhshi, Kaveh, Taghinezhad, & Sharabiani, 2020; Kaveh, Jahanbakhshi, Abbaspour‐Gilandeh, Taghinezhad, & Moghimi, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Influence of ultrasound pre‐treatment and temperature on the quality and thermodynamic properties in the drying process of nectarine slices in a hot air dryer

Jahanbakhshi

Yeganeh

Momeny

2020

J. Food Process. Preserv.

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Drying is one of the ways to reduce postharvest waste and processing in agricultural products. Drying with hot air is one of the most popular drying methods in the food industry. The purpose of this study is to investigate the effect of ultrasound and temperature on the quality and thermodynamic properties in the process of drying nectarine slices in a hot air dryer. The drying process was performed at four levels of ultrasonic pre-treatment of 0 min (control sample), 10, 20, and 40 min and three temperature levels of 50, 60, and 75°C. Experiments were performed on 5 mm thick How to cite this article: Jahanbakhshi A, Yeganeh R, Momeny M. Influence of ultrasound pre-treatment and temperature on the quality and thermodynamic properties in the drying process of nectarine slices in a hot air dryer.

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“…The ultrasound power (UP) was calculated using Equation (18) (Jahanbakhshi et al., 2020; Kaveh et al., 2018).

UP = UI \cos Φ

…”

Section: Methodsmentioning

confidence: 99%

{SEC}_{ult} = \frac{UP . t}{m_{vult}}

…”

Section: Methodsmentioning

confidence: 99%

{SEC}_{total} = {SEC}_{ult} + {SEC}_{oven}

…”

Section: Methodsmentioning

confidence: 99%

“…Equation 22, the total specific energy consumption was calculated (Jahanbakhshi et al, 2020;Kaveh et al, 2018):…”

Section: Specific Energy Consumption Of Hot Air (Oven) Dryermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Influence of ultrasound pre‐treatment and temperature on the quality and thermodynamic properties in the drying process of nectarine slices in a hot air dryer

Jahanbakhshi

Yeganeh

Momeny

2020

J. Food Process. Preserv.

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Variables related to microwave heating‐toasting time and water migration assessment with kernel size approaches of specialty maize types

2022

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BACKGROUND Three main maize types with specialty kernels are used to make ready‐to‐eat maize by traditional toasting, and microwave toasting may be an innovative application. However, little is known of the toasting process of these Andean maize types. Therefore, the present study aimed to explore the behavior of a broad scope of variables in these maize types. The kernels were packed in sealed paper envelopes and subjected to six microwave heating‐toasting times from 0 to 390 s. Subsequently, with actual kernel size approaches, water content (WC), water ratio (WR), and water loss (WL) were analyzed. RESULTS In addition to WC, WR, and WL, the surface area (S), volume (V), and geometric mean diameter (GMD) behaved like time‐related variables with a high correlation depending on the maize types and kernel dimensions. Thus, the WC, WR, and WL third‐order polynomial regression curves computed with the spatial (S/V)2 and distance (GMD/2)2 approaches indicated the water variation at each microwave heating‐toasting time with a clear difference among maize types a0, a1, and a2. Regarding their exchange profiles without and with the spatial (S/V)2 approach, the maximum rates showed significant differences between maize types and WC and WL. Likewise, the maximum rates displayed significant differences between the spatial (S/V)2 and distance (GMD/2)2 approaches, revealing a notable lack of consistency with the distance (GMD/2)2 approach. CONCLUSION The kernel size approaches revealed that water migration rates depended on differences in maize types. Such basic information represents the first insight into more physical‐based models of water diffusion during raw microwave maize heating‐toasting. © 2022 Society of Chemical Industry.

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Recent application of artificial neural network in microwave drying of foods: a mini‐review

Yang

Chen

2022

J Sci Food Agric

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The microwave-assisted thermal process is a high-efficiency drying method and is promising to be applied in the food industry. However, the prediction of the thermal treatment results from such a dynamic and complicated process can be difficult. Additionally, the determination of the optimal drying parameters, such as drying temperature, microwave power, and drying time for optimized performance can also be hard. Recently, extensive research has been focusing on the use of artificial neural network (ANN) models in the laboratory-scale microwave drying processes and has shown the feasibility of such application. As a regression tool, the ANN models have been widely used in predicting drying performance; when integrated with additional optimizing algorithms, the ANN models could be used for drying parameter optimization; and when combined with real-time measuring techniques (e.g. nuclear magnetic resonance), the ANN models could be used for monitoring and controlling the drying process in a dynamic sense. Future research could focus on testing the developed ANN models in industrial-scale microwave drying processes and applying the ANN models in microwave drying kinetics research for optimizing the dynamic drying processes.

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

Cited by 66 publications

References 68 publications

Influence of ultrasound pre‐treatment and temperature on the quality and thermodynamic properties in the drying process of nectarine slices in a hot air dryer

Influence of ultrasound pre‐treatment and temperature on the quality and thermodynamic properties in the drying process of nectarine slices in a hot air dryer

Variables related to microwave heating‐toasting time and water migration assessment with kernel size approaches of specialty maize types

Recent application of artificial neural network in microwave drying of foods: a mini‐review

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