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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics
Sumit Goyal
National Dairy Research Institute, Karnal
Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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 1 
Soft Computing Methodology for Shelf Life Prediction of Processed Cheese Sumit Goyal; Gyanendra Kumar Goyal
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 1, No 1: June 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (493.301 KB)

Abstract

Feedforward multilayer models were developed for predicting shelf life of processed cheese stored at 30o C. Input variables were Soluble nitrogen, pH, Standard plate count, Yeast & mould count and Spore count. Sensory score was taken as output parameter for developing feedforward multilayer models. Mean square error, root mean square error, coefficient of determination and nash - sutcliffo coefficient performance measures were implemented for testing prediction potential of the soft computing models. The study revealed that soft computing multilayer models can predict shelf life of processed cheese.DOI: http://dx.doi.org/10.11591/ij-ict.v1i1.506
Dynamic Scientific Method for Predicting Shelf Life of Buffalo Milk Dairy Product Sumit Goyal; Gyanendra Kumar Goyal
International Journal of Advances in Applied Sciences Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (89.087 KB) | DOI: 10.11591/ijaas.v1.i1.pp29-34

Abstract

Feedforward multilayer machine learning models were developed to predict the shelf life of burfi stored at 30oC. Experimental data of the product relating to moisture, titratable acidity, free fatty acids, tyrosine, and peroxide value were input variables, and the overall acceptability score was the output. Bayesian regularization algorithm was used for training the network. The transfer function for hidden layers was tangent sigmoid, and for the output layer it was purelinear function. The network was trained with 100 epochs, and neurons in each hidden layers varied from 3:3 to 20:20. Excellent agreement was found between the actual and predicted values establishing that feedforward multilayer machine learning models are efficient in predicting the shelf life of burfi.
Cascade modelling for predicting solubility index of roller dried goat whole milk powder Sumit Goyal; Gyanendra Kumar Goyal
Bulletin of Electrical Engineering and Informatics Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (52.951 KB) | DOI: 10.11591/eei.v2i1.257

Abstract

The aim of this work was to investigate the prediction ability of Cascade artificial neural network (ANN) models for solubility index of roller dried goat whole milk powder. The input variables for ANN model were: loose bulk density, packed bulk density, wettability and dispersibility, while solubility index was the output variable. Mean square error, root mean square error, coefficient of determination and Nash - sutcliffo coefficient were used as performance measures. Modelling results indicated very good agreement between the actual and the predicted data, thus confirming that ANN could be used to predict solubility index of roller dried goat whole milk powder.
Cascade modelling for predicting solubility index of roller dried goat whole milk powder Sumit Goyal; Gyanendra Kumar Goyal
Bulletin of Electrical Engineering and Informatics Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v2i1.257

Abstract

The aim of this work was to investigate the prediction ability of Cascade artificial neural network (ANN) models for solubility index of roller dried goat whole milk powder. The input variables for ANN model were: loose bulk density, packed bulk density, wettability and dispersibility, while solubility index was the output variable. Mean square error, root mean square error, coefficient of determination and Nash - sutcliffo coefficient were used as performance measures. Modelling results indicated very good agreement between the actual and the predicted data, thus confirming that ANN could be used to predict solubility index of roller dried goat whole milk powder.
Cascade modelling for predicting solubility index of roller dried goat whole milk powder Sumit Goyal; Gyanendra Kumar Goyal
Bulletin of Electrical Engineering and Informatics Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (52.951 KB) | DOI: 10.11591/eei.v2i1.257

Abstract

The aim of this work was to investigate the prediction ability of Cascade artificial neural network (ANN) models for solubility index of roller dried goat whole milk powder. The input variables for ANN model were: loose bulk density, packed bulk density, wettability and dispersibility, while solubility index was the output variable. Mean square error, root mean square error, coefficient of determination and Nash - sutcliffo coefficient were used as performance measures. Modelling results indicated very good agreement between the actual and the predicted data, thus confirming that ANN could be used to predict solubility index of roller dried goat whole milk powder.
Co-Authors Gyanendra Kumar Goyal