Informatics Publishing Limited

Anil P. Joshi , Subodh K. Gupta , Arun K. Agrawal , A. Chaudhary

Abstract

A tolal of 30 species were found in the forest floor of Pinus roxburghil Sarg. Grasses dominating over dicots, dominating species being Eragrostis brachyphylla. E. pilosa and Eriophorum gracile. Nineteen out of 30 species were found associated. The association coefficient for significantly associated species varied from + 0.63 to + 0.98 for positive and −0.466 to − 0.777 for negative associations. Densities of species pairs correlated significantly. These species were further analysed for regression equation.

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S. Goyal ¹, A. Chaudhary ²

Affiliations
1 Mewar University, Chittorgarh, Rajasthan, IN
2 Garg College of Engineering, Ghaziabad, Uttar Pradesh, IN

Abstract

A predictive model for predicting shelf life of modified atmosphere (MA) packed paneer using feedforward artificial neural network is proposed. Feedforward networks with single and double hidden layers were developed with Bayesian regularization. The best fitting for single hidden layer was obtained with 4→24→1, and for the double hidden layers with combination of 4→27→27→1 topology, which made it possible to predict the overall acceptability with accuracy. The developed model can be used for predicting the shelf life of MA packed paneer.

Keywords

Feedforward, Shelf Life, Paneer, Artificial Neural Networks.

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S. Goyal , A. Chaudhary

Abstract

Artificial neural network model was developed for predicting shelf life of MA packed paneer. Levenberg-Marquardt algorithm along with Feedforward backpropagation was used for experimentation. Moisture, titratable acidity, free fatty acids and tyrosine were used as input parameters, while overall acceptability was taken as the output parameter for developing feedforward ANN models. Data was divided into two disjoint sets, viz. 80% used for training and 20% for testing. The combination of 4à45à1 gave the best fitting, suggesting that developed model has the potential for predicting shelf life of MA packed paneer with accuracy.

Keywords

Feedforward, Shelf Life, Machine Learning, Artificial Neural Networks

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