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. 2020 Mar 9;9(3):308.
doi: 10.3390/foods9030308.

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

S M Atiqure Rahman  1 , Ahmed M Nassef  2   3 , Mujahed Al-Dhaifallah  4 , Mohammad Ali Abdelkareem  1   5   6 , Hegazy Rezk  2   7
Affiliations

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

S M Atiqure Rahman et al. Foods. .

Abstract

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.

Keywords: alginic and polygalacturonic acid; artificial neural network; drying performance; edible coating; glucose and sucrose; modeling; osmotic dehydration.

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Conflict of interest statement

No conflict of interest.

Figures

Figure 1
Figure 1
(a) Nonlinear model of a neuron (labeled k). (b) Architectural graph of a multilayer perceptron with two hidden layers.
Figure 2
Figure 2
Variation of (a) water loss and (b) solid gain with time for potato sample.
Figure 3
Figure 3
Variation of dimensionless moisture content with drying time.
Figure 4
Figure 4
Variations of (a) drying rate with dimensionless moisture content and (b) time for potato samples.
Figure 5
Figure 5
Variations of (a) water loss and (b) solid gain with time for apple samples.
Figure 6
Figure 6
Variations of (a) drying rate with dimensionless moisture content and (b) time for apple samples.
Figure 7
Figure 7
(a) Training performance of the potato artificial neural network (ANN) model. (b) Prediction accuracy of the potato ANN model.
Figure 8
Figure 8
(a) Training performance of the apple ANN model. (b) Prediction accuracy of the apple ANN model.
Figure 9
Figure 9
Experimental and ANN outputs of the water loss and solid gain of the potato model.
Figure 10
Figure 10
Experimental and ANN outputs of the water loss and solid gain of the apple model.
Figure 11
Figure 11
Potato model predictions, including the extended time.
Figure 12
Figure 12
Apple model predictions, including the extended time.
See this image and copyright information in PMC

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