Application of Simulated Neural Networks as Non-Linear Modular Modeling Method for Predicting Shelf Life of Processed Cheese

Sumit Goyal, Gyanendra Kumar Goyal


This paper presents the capability of simulated neural network (SNN) models for predicting the shelf life of processed cheese
stored at ambient temperature 30o C. Processed cheese is a dairy product generally made from medium ripened Cheddar
cheese. Elman and Linear Layer(Train) SNN models were developed. Body & texture, aroma & flavour, moisture, free fatty
acids were used as input variables and sensory score as the output. Neurons in each hidden layers varied from 1 to 40. The
network was trained with single as well as double hidden layers up to 100 epochs, and transfer function for hidden layer was
tangent sigmoid while for the output layer, it was pure linear function. Mean square error, root mean square error, coefficient
of determination and nash - sutcliffo coefficient performance measures were used for testing prediction potential of the
developed models. Results showed a 4201 topology was able to predict the shelf life of processed cheese exceedingly well
with R2 as 0.99992157. The corresponding RMSE for this topology was 0.003615359. From this study it is concluded that SNN
models are excellent tool for predicting the shelf life of processed cheese.

Full Text:



  • There are currently no refbacks.

Copyright (c) 2015 Sumit Goyal, Gyanendra Kumar Goyal

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Universiti Teknologi MARA Cawangan Perlis