Comparative Study of Response Surface Methodology, Artificial Neural Network and Genetic Algorithms for Optimization of Soybean Hydration
The present investigation deals with the modelling and optimization of soybean hydration for facilitating soybean processing and it focuses on maximization of mass gain, water uptake and protein retention in the bean. Process variables considered for optimization were: soybean to water ratio (1:2.48...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Zagreb Faculty of Food Technology and Biotechnology
2010-01-01
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| Series: | Food Technology and Biotechnology |
| Subjects: | |
| Online Access: | http://hrcak.srce.hr/file/74725 |
| Summary: | The present investigation deals with the modelling and optimization of soybean hydration for facilitating soybean processing and it focuses on maximization of mass gain, water uptake and protein retention in the bean. Process variables considered for optimization were: soybean to water ratio (1:2.48 obtained with response surface methodology, RSM, and 1.19 obtained with artificial neural network and genetic algorithm, ANN/GA), time (2.0 h using RSM and 8.0 h using ANN/GA) and temperature (40.0 °C using RSM and 45.1 °C using ANN/GA). The findings in this first report on optimization of soaking conditions for soybean hydration employing response surface methodology, hybrid artificial neural network and genetic algorithms reveal a substantially better alternative to the time-consuming soaking process, extensively practiced in industries, in terms of process time economy. Reasonably accurate neural network model (regression coefficient of 0.9443) was obtained based on the experimental data. The optimized set of process conditions was predicted through genetic algorithm, and the effectiveness of the ANN/GA model, validated through experiments, was indicated by significant correlations (R2 and mean squared error (MSE) being 0.9380 and 5.9299, respectively). RSM also resulted in accurate models for predicting percentage mass gain, percentage water uptake and percentage protein retention (R2 and MSE in the range of 0.889–0.9297 and 0.80–4.94, respectively). |
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| ISSN: | 1330-9862 1334-2606 |