Evaluation of Shelf Life of Processed Cheese by Implementing Neural Computing Models

For predicting the shelf life of processed cheese stored at 7-8º C, Elman single and multilayer models were developed and compared. The input variables used for developing the models were soluble nitrogen, pH; standard plate count, Yeast & mould count, and spore count, while output variable was...

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Main Authors: Sumit Goyal, Gyanendra Kumar Goyal
Format: Article
Language:English
Published: Universidad Internacional de La Rioja (UNIR) 2012-06-01
Series:International Journal of Interactive Multimedia and Artificial Intelligence
Subjects:
Online Access:http://www.ijimai.org/journal/sites/default/files/IJIMAI20121_5_8.pdf
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author Sumit Goyal
Gyanendra Kumar Goyal
author_facet Sumit Goyal
Gyanendra Kumar Goyal
author_sort Sumit Goyal
collection DOAJ
description For predicting the shelf life of processed cheese stored at 7-8º C, Elman single and multilayer models were developed and compared. The input variables used for developing the models were soluble nitrogen, pH; standard plate count, Yeast & mould count, and spore count, while output variable was sensory score. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were applied in order to compare the prediction ability of the developed models. The Elman models got simulated very well and showed excellent agreement between the experimental data and the predicted values, suggesting that the Elman models can be used for predicting the shelf life of processed cheese.
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spelling doaj.art-c21472d9f8e74fab98eaa583dc2ff5422022-12-22T00:37:14ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16602012-06-01156164Evaluation of Shelf Life of Processed Cheese by Implementing Neural Computing ModelsSumit GoyalGyanendra Kumar GoyalFor predicting the shelf life of processed cheese stored at 7-8º C, Elman single and multilayer models were developed and compared. The input variables used for developing the models were soluble nitrogen, pH; standard plate count, Yeast & mould count, and spore count, while output variable was sensory score. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were applied in order to compare the prediction ability of the developed models. The Elman models got simulated very well and showed excellent agreement between the experimental data and the predicted values, suggesting that the Elman models can be used for predicting the shelf life of processed cheese.http://www.ijimai.org/journal/sites/default/files/IJIMAI20121_5_8.pdfArtificial Neural NetworkArtificial IntelligenceElmanProcessed CheeseShelf Life
spellingShingle Sumit Goyal
Gyanendra Kumar Goyal
Evaluation of Shelf Life of Processed Cheese by Implementing Neural Computing Models
International Journal of Interactive Multimedia and Artificial Intelligence
Artificial Neural Network
Artificial Intelligence
Elman
Processed Cheese
Shelf Life
title Evaluation of Shelf Life of Processed Cheese by Implementing Neural Computing Models
title_full Evaluation of Shelf Life of Processed Cheese by Implementing Neural Computing Models
title_fullStr Evaluation of Shelf Life of Processed Cheese by Implementing Neural Computing Models
title_full_unstemmed Evaluation of Shelf Life of Processed Cheese by Implementing Neural Computing Models
title_short Evaluation of Shelf Life of Processed Cheese by Implementing Neural Computing Models
title_sort evaluation of shelf life of processed cheese by implementing neural computing models
topic Artificial Neural Network
Artificial Intelligence
Elman
Processed Cheese
Shelf Life
url http://www.ijimai.org/journal/sites/default/files/IJIMAI20121_5_8.pdf
work_keys_str_mv AT sumitgoyal evaluationofshelflifeofprocessedcheesebyimplementingneuralcomputingmodels
AT gyanendrakumargoyal evaluationofshelflifeofprocessedcheesebyimplementingneuralcomputingmodels