Machine Learning ANN Models for Predicting Sensory Quality of Roasted Coffee Flavoured Sterilized Drink

General information

Authors:
Sumit GOYAL, Gyanendra Kumar GOYAL

DOI:
10.14201/ADCAIJ201426913

Volume:
Regular Issue 2 (3), 2013

Keywords:

Artificial neural network; Machine learning; Feedforward; Shelf life prediction; Bayesian regularization; Roasted coffee; Sensory quality

This paper highlights the significance of feedforward artificial neural network models for predicting shelf life of roasted coffee falvoured sterilized drink. Coffee is one of the most important products for trade in international market. Single as well as multilayer models were explored and different backpropagation algorithms were investigated, Root mean square error and coefficient of determination R2 were used to compare the prediction performance of single and multilayer feedforward ANN models. Experimental results suggested that multilayer models take less time and give better results as compared to single layer ANN models for prediction of sensory quality of roasted coffee falvoured sterilized drink..

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