scholarly journals Modeling of Osmotic Dehydration Kinetics of Banana Slices using Artificial Neural Network

2012 ◽  
Vol 48 (3) ◽  
pp. 26-31
Author(s):  
S. L.Pandharipande ◽  
Saurav Paul ◽  
Ankit Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Osmotic Dehydration ◽  
Dehydration Kinetics ◽  
Artificial Neural ◽  
Kinetics Of

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

Foods ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 308
Author(s):  
S. M. Atiqure Rahman ◽  
Ahmed M. Nassef ◽  
Mujahed Al-Dhaifallah ◽  
Mohammad Ali Abdelkareem ◽  
Hegazy Rezk
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Loss ◽  
Alginic Acid ◽  
Osmotic Dehydration ◽  
Neural Network Modeling ◽  
Polygalacturonic Acid ◽  
Coating Materials ◽  
Solid Gain ◽  
Artificial Neural

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.

scholarly journals Artificial neural network model of pork meat cubes osmotic dehydratation

2013 ◽  
Vol 67 (3) ◽  
pp. 465-475 ◽  
Author(s):  
Lato Pezo ◽  
Biljana Curcic ◽  
Vladimir Filipovic ◽  
Milica Nicetin ◽  
Gordana Koprivica ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Osmotic Dehydration ◽  
Coefficient Of Determination ◽  
Dry Matter Content ◽  
Pork Meat ◽  
Triceps Brachii ◽  
Solid Gain ◽  
Wide Range ◽  
Artificial Neural

Mass transfer of pork meat cubes (M. triceps brachii), shaped as 1x1x1 cm, during osmotic dehydration (OD) and under atmospheric pressure was investigated in this paper. The effects of different parameters, such as concentration of sugar beet molasses (60-80%, w/w), temperature (20-50?C), and immersion time (1-5 h) in terms of water loss (WL), solid gain (SG), final dry matter content (DM), and water activity (aw), were investigated using experimental results. Five artificial neural network (ANN) models were developed for the prediction of WL, SG, DM, and aw in OD of pork meat cubes. These models were able to predict process outputs with coefficient of determination, r2, of 0.990 for SG, 0.985 for WL, 0.986 for aw, and 0.992 for DM compared to experimental measurements. The wide range of processing variables considered for the formulation of these models, and their easy implementation in a spreadsheet calculus make it very useful and practical for process design and control.

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