Generalized Regression Models Predicting Shelf Life of Roasted Coffee Sterilized Drink

Sumit Goyal, Gyanendra Kumar Goyal


Artificial neural network (ANN) model was developed for predicting the shelf life of packaged roasted coffee sterilized milk drink stored at 30oC. The input parameters were colour and appearance, flavour, viscosity, and sediment. The overall acceptability score was taken as the output parameter. Mean square error, root mean square error, coefficient of determination and Nash-Sutcliffe coefficient were used as performance evaluators. The experimental results showed that there was excellent agreement between the experimental data and the predicted values with high coefficient of determination. ANN models predicted shelf life of roasted coffee sterilized drink very well. These results support the claim that an ANN approach is good for predictive modelling in packaged food products.


shelf life, roasted coffee sterilized drink, packaging, artificial intelligence, artificial neural networks, generalized regression

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