A Novel Vortex Tube‐Assisted Atmospheric Freeze‐Drying System: Effect of Osmotic Pretreatment on Biological Products

Rahman, S. M. A.; Hoque, M. Enamul; Saidur, R.; Rahman, Mohammad Mahbubur

doi:10.1111/jfpe.12449

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…As the process works under diffusion, this drying process can be employed at low temperatures and thus consumes less energy compared to the convective drying process [16][17][18]. In addition to reduced energy consumption, this method reduces the browning of food products caused by the enzymes and also enhances the color and the flavor retention [19,20] with reduced water activity [21]. OD's mechanism is represented in terms of water loss, weight loss, and solids gain [22,23].…”

Section: Introductionmentioning

confidence: 99%

Predicting Drying Performance of Osmotically Treated Heat Sensitive Products Using Artificial Intelligence

Rahman¹,

Rezk²,

Abdelkareem³

et al. 2021

Computers, Materials &Amp; Continua

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The main goal of this research is to develop and apply a robust Artificial Neural Networks (ANNs) model for predicting the characteristics of the osmotically drying treated potato and apple samples as a model heat-sensitive product in vacuum contact dryer. Concentrated salt and sugar solutions were used as the osmotic solutions at 27 • C. Series of experiments were performed at various temperatures of 35 • C, 40 • C, and 55 • C for conduction heat input under vacuum (−760 mm Hg) condition. Some experiments were also performed in a pure vacuum without heat addition. Dimensionless moisture content (DMC), effective moisture diffusivity, and mass flux were considered as the performance parameters in this study. Results revealed that the osmotic dehydration using a concentrated sugar solution shows a higher reduction in the initial moisture loss of 19.87% compared to 5.3% in the salt solution. Furthermore, a significant enhancement of drying performance of about 27% in DMC was observed for both samples at vacuum and 40 • C compared to pure vacuum drying conditions. Using the experimental data, a robust artificial neural network (ANN) was proposed to describe the osmotic dehydration's behavior on the drying process. The ANN model outputs are the dimensionless moisture contents (DMC), the diffusivity, and the mass flux. Whereas the ANN inputs were the drying time, the percent of sugar solution, and the percent of salt solution. For the ANN apple's model, the minimum root mean square error (RMSE) values were 0.0261, 0.0349 and 0.0406, for DMC, diffusivity, and mass flux, respectively. Whereas the best correlation coefficients of the above three parameters' determination values were 0.9909, 0.9867 and 0.9744, respectively. For the ANN potato's model, the minimum RMSE values were 0.0124, 0.0140 and 0.0333, for DMC, diffusivity, and mass flux, respectively. And the best correlation coefficients of This work is licensed under a Creative Commons Attribution 4.

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Section: Introductionmentioning

confidence: 99%

Predicting Drying Performance of Osmotically Treated Heat Sensitive Products Using Artificial Intelligence

Rahman¹,

Rezk²,

Abdelkareem³

et al. 2021

Computers, Materials &Amp; Continua

Self Cite

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“…It is a useful drying technique that is used prior to a regular drying process. Sugar and salt aqueous solutions at high osmotic pressure are some of the suitable options for osmotic drying of fruits and vegetables, whereby the solid product either (whole or in pieces) is placed into the solution [8,9]. This gives rise to at least two major simultaneous counter-current flows, namely water flowing out of the food into the solution and a simultaneous transfer of a solute from the solution into the food, which are both due to water and solute activity gradients across the cell membrane [3,[10][11][12].…”

Section: Introductionmentioning

confidence: 99%

The Effect of a New Coating on the Drying Performance of Fruit and Vegetables Products: Experimental Investigation and Artificial Neural Network Modeling

Rahman

Nassef

Al‐Dhaifallah

et al. 2020

Foods

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A study on mass transfer using new coating materials (namely alginic acid and polygalacturonic acid) during osmotic dehydration—and hence in a laboratory-scale convective dryer to evaluate drying performance—was carried out. Potato and apple samples were examined as model heat-sensitive products in this study. Results indicate that the coating material containing both alginic acid and polygalacturonic acid causes higher water loss of about 17% and 7.5% and lower solid gain of about 4% and 8%, respectively, compared to uncoated potato sample after a typical 90 min osmotic dehydration process. Investigation of drying performance using both coating materials showed a higher reduction in the moisture content of about 22% and 18%, respectively, compared with uncoated samples after the 3 h drying period. Comparisons between the two proposed coating materials were also carried out. Samples (potato) coated with alginic acid demonstrated better performance in terms of higher water loss (WL), lower solid gain (SG), and notable enhancement of drying performance of about 7.5%, 8%, and 8%, respectively, compared to polygalacturonic acid. Similar outcomes were observed using apple samples. Additionally, an accurate model of the drying process based on the experimental dataset was created using an artificial neural network (ANN). The obtained mean square errors (MSEs) for the predicted water loss and solid gain outputs of the potato model were 4.0948e−5 and 3.924e−6, respectively. However, these values for the same parameters were 3.164e−5 and 4.4915e−6 for the apple model. The coefficient of determination (r2) values for the two outputs of the potato model were found to be 0.99969 and 0.99895, respectively, while they were 0.99982 and 0.99913 for the apple model, which reinforces the modeling phase.

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“…This encourages the industry stakeholders to find a way to store/preserve these fruits for a longer time duration where it can be done by a proper drying process. In order to achieve suitable quality, the choice of the appropriate drying conditions play a significant role in conserving the fruits of such interest [6]. Over the years, several drying processes, used for drying/preserving fruits that include osmotic drying, have shown great success [7].…”

Section: Introductionmentioning

confidence: 99%

ANFIS-Based Modelling and Optimal Operating Parameter Determination to Enhance Cocoa Beans Drying-Rate

et al. 2020

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This work aims to maximize the drying-rate of the cocoa beans by controlling the values of temperature, pressure, and moisture contents. The drying-rate is maximized by defining the optimal operating conditions. Using the experimental data, obtained from the drying-rate process, a robust model that describes the drying-rate of the cocoa beans is generated using fuzzy logic approach. Then, the optimal operating conditions of the system are determined using Particle Swarm Optimization (PSO) algorithm. Three different operating parameters that influence the drying rate of the cocoa beans are assessed. These parameters are the temperature (• C), pressure (in.Hg), and moisture contents. Accordingly, during the optimization process, these parameters are used as the decision variables for the PSO optimizer in order to maximize the drying-rate which is used as a cost function. The resulting plots demonstrated a well-fitting between the output of the fuzzy model and the experimental data. The mean square errors of the resulting predictions are found 0.000297 and 0.00012 for testing and whole datasets, respectively. Based on the built model, the optimization process achieved a significant increase in the drying-rate to a value of 2.12 relative to 1.315 which was obtained experimentally. This proved that the fuzzy modelling with PSO attained an increase of 61.14% without changing the system's design or the materials used. Indeed, the originality of this research work promotes the proposed model to be a valuable tool for the design of future dryers. INDEX TERMS Fuzzy modelling, optimization, vacuum drying, cocoa beans.

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A Novel Vortex Tube‐Assisted Atmospheric Freeze‐Drying System: Effect of Osmotic Pretreatment on Biological Products

Cited by 6 publications

References 26 publications

Predicting Drying Performance of Osmotically Treated Heat Sensitive Products Using Artificial Intelligence

Predicting Drying Performance of Osmotically Treated Heat Sensitive Products Using Artificial Intelligence

The Effect of a New Coating on the Drying Performance of Fruit and Vegetables Products: Experimental Investigation and Artificial Neural Network Modeling

ANFIS-Based Modelling and Optimal Operating Parameter Determination to Enhance Cocoa Beans Drying-Rate

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