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 Predicting the drying kinetics of salted codfish (Gadus Morhua): semi-empirical, diffusive and neural network models
Please use this identifier to cite or link to this item http://hdl.handle.net/10773/6989

title: Predicting the drying kinetics of salted codfish (Gadus Morhua): semi-empirical, diffusive and neural network models
authors: Boeri, Camila
Neto da Silva, Fernando
Ferreira, Jorge
Saraiva, Jorge
Salvador, Ângelo
keywords: Artificial neural network
codfish drying
comparison of mathematical models
Fick diffusion law
semi-empirical models
issue date: 2011
publisher: Wiley-Blackwell
abstract: This work aims to compare the accuracy of several drying modelling techniques namely semi-empirical, diffusive and artificial neural network (ANN) models as applied to salted codfish (Gadus Morhua). To this end, sets of experimental data were collected to adjust parameters for the models. Modelling of codfish drying was performed by resorting to Page and Thompson semi-empirical models and to a Fick diffusion law. The ANN employed a neural network multilayer ‘feed-forward’, consisting of one input layer, with four neurons, one hidden layer, formed by five neurons and one output layer with a convergence criterion for training purposes. The simulations showed good results for the ANN (correlation coefficient between 0.987 and 0.999) and semi-empirical models (correlation coefficient ranging from 0.992 to 0.997 for Page’s model, and from 0.993 to 0.996 for Thompson’s model), while improvements were required to obtain better predictions by the diffusion model (correlation coefficients ranged from 0.864 to 0.959).
URI: http://hdl.handle.net/10773/6989
ISSN: 0950-5423
publisher version/DOI: http://dx.doi.org/10.1111/j.1365-2621.2010.02513.x
source: International Journal of Food Science and Technology
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