Use of artificial neural network and image analysis to predict physical properties of osmotically dehydrated pumpkin
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Publication Details
Author list: Zenoozian M.S., Devahastin S., Razavi M.A., Shahidi F., Poreza H.R.
Publisher: Taylor and Francis Group
Publication year: 2008
Journal: Drying Technology (0737-3937)
Volume number: 26
Issue number: 1
Start page: 132
End page: 144
Number of pages: 13
ISSN: 0737-3937
eISSN: 1532-2300
Languages: English-Great Britain (EN-GB)
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Abstract
The objectives of this research were to predict, using neural networks, the color intensity (ΔE), percentage of shrinkage as well as the Heywood shape factor, which is the representative of deformation, of osmotically dehydrated and air dried pumpkin pieces. Several osmotic solutions were used including 50% (w/w) sorbitol solution, 50% (w/w) glucose solution, and 50% (w/w) sucrose solution. Optimum artificial neural network (ANN) models were developed based on one to two hidden layers and 10-20 neurons per hidden layer. The ANN models were then tested against an independent data set. The measured values of the color intensity, percentage of shrinkage, and the Heywood shape factor were predicted with R2 > 0.90 in all cases, except when all the drying methods were combined in one data set.
Keywords
Color, Deformation, Heywood shape factor, Osmotic dehydration, Shrinkage
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