Application of artificial neural engineering and regression models for forecasting shelf life of instant coffee drink
Coffee as beverage is prepared from the roasted seeds
(beans) of the coffee plant. Coffee is the second most
important product in the international market in terms
of volume trade and the most important in terms of
value. Artificial neural engineering and regression
models were developed to predict shelf life of instant
coffee drink. Colour and appearance, flavour,
viscosity and sediment were used as input parameters.
Overall acceptability was used as output parameter.
The dataset consisted of experimentally developed 50
observations. The dataset was divided into two
disjoint subsets, namely, training set containing 40
observations (80% of total observations) and test set
comprising of 10 observations (20% of total
observations). The network was trained with 500
epochs. Neural network toolbox under Matlab 7.0
software was used for training the models. From the
investigation it was revealed that multiple linear
regression model was superior over radial basis
model for forecasting shelf life of instant coffee drink.
Keywords: artificial neural engineering, instant coffee drink, regression, neurons, shelf life
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