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Modelling moisture sorption characteristics in dried acid casein using connectionist paradigm vis-à-vis classical methods.

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Title Modelling moisture sorption characteristics in dried acid casein using connectionist paradigm vis-à-vis classical methods.
Not Available
 
Creator A.K. Sharma
I.K. Sawhney
 
Subject Buffalo
Casein
Connectionist models
Milk
Prediction
Sorption characteristics
Empirical sorption models
 
Description Institute funded research project (IRC)
Six connectionist models are proposed to predict sorption (adsorption and desorption) characteristics at three temperatures, i.e., 25°, 35° and 45 °C over a water activity range of 0.11–0.97 in dried acid casein prepared from buffalo skim milk. Also, several conventional empirical sorption models were used for fitting the sorption data. The dataset comprised of 210 data points. The Error Back Propagation (EBP) learning algorithm with Bayesian Regularisation/Levenberg-Marquardt optimisation techniques as well as various combinations of connectionist network parameters was employed. The connectionist models predicted the adsorption characteristics with an accuracy ranging between 1.32 and 2.60 Root Mean Square percent error (RMS%) as compared to the best (among six conventional empirical models used) GAB model, which attained RMS% between 1.92 and 5.77. While for desorption characteristics, the connectionist models attained RMS% between 1.56 and 4.08 vis-à-vis the best (among six conventional empirical models used) GAB model with RMS% ranging between 1.4 and 5.01. Hence, the results revealed that the connectionist models outperformed the conventional empirical models and, generally, best described the experimental adsorption and desorption data for dried acid casein prepared from buffalo skim milk.
Not Available
 
Date 2020-04-26T15:56:58Z
2020-04-26T15:56:58Z
2013-04-09
 
Type Research Paper
 
Identifier Sharma, A.K., Sawhney, I.K. Modelling moisture sorption characteristics in dried acid casein using connectionist paradigm vis-à-vis classical methods. J Food Sci Technol 52, 151–160 (2015). https://doi.org/10.1007/s13197-013-0981-3.
0022-1155 (Print)
http://krishi.icar.gov.in/jspui/handle/123456789/35128
 
Language English
 
Relation Not Available;
 
Publisher Springer Nature Switzerland AG.