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...
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University of Zagreb Faculty of Food Technology and Biotechnology
2010-01-01
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| Series: | Food Technology and Biotechnology |
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| Online Access: | http://hrcak.srce.hr/file/74725 |
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| author | Tushar Gulati Mainak Chakrabarti Anshu Sing Anshu Sing Muralidhar Duvuuri Rintu Banerjee |
| author_facet | Tushar Gulati Mainak Chakrabarti Anshu Sing Anshu Sing Muralidhar Duvuuri Rintu Banerjee |
| author_sort | Tushar Gulati |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-09T09:10:19Z |
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| institution | Directory Open Access Journal |
| issn | 1330-9862 1334-2606 |
| language | English |
| last_indexed | 2024-03-09T09:10:19Z |
| publishDate | 2010-01-01 |
| publisher | University of Zagreb Faculty of Food Technology and Biotechnology |
| record_format | Article |
| series | Food Technology and Biotechnology |
| spelling | doaj.art-c54cb54b2e5441169eebc805112e77db2023-12-02T09:04:37ZengUniversity of Zagreb Faculty of Food Technology and BiotechnologyFood Technology and Biotechnology1330-98621334-26062010-01-014811118Comparative Study of Response Surface Methodology, Artificial Neural Network and Genetic Algorithms for Optimization of Soybean HydrationTushar Gulati0Mainak Chakrabarti1Anshu Sing2Anshu Sing3Muralidhar Duvuuri4Rintu Banerjee5Microbial Biotechnology and Downstream Processing Laboratory, Agricultural and Food Engineering Department, Indian Institute of Technology, IN-721302 Kharagpur, IndiaMicrobial Biotechnology and Downstream Processing Laboratory, Agricultural and Food Engineering Department, Indian Institute of Technology, IN-721302 Kharagpur, IndiaMicrobial Biotechnology and Downstream Processing Laboratory, Agricultural and Food Engineering Department, Indian Institute of Technology, IN-721302 Kharagpur, IndiaMicrobial Biotechnology and Downstream Processing Laboratory, Agricultural and Food Engineering Department, Indian Institute of Technology, IN-721302 Kharagpur, IndiaMicrobial Biotechnology and Downstream Processing Laboratory, Agricultural and Food Engineering Department, Indian Institute of Technology, IN-721302 Kharagpur, IndiaMicrobial Biotechnology and Downstream Processing Laboratory, Agricultural and Food Engineering Department, Indian Institute of Technology, IN-721302 Kharagpur, IndiaThe 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).http://hrcak.srce.hr/file/74725response surface methodology (RSM)artificial neural network (ANN)genetic algorithms (GA)soybean soaking |
| spellingShingle | Tushar Gulati Mainak Chakrabarti Anshu Sing Anshu Sing Muralidhar Duvuuri Rintu Banerjee Comparative Study of Response Surface Methodology, Artificial Neural Network and Genetic Algorithms for Optimization of Soybean Hydration Food Technology and Biotechnology response surface methodology (RSM) artificial neural network (ANN) genetic algorithms (GA) soybean soaking |
| title | Comparative Study of Response Surface Methodology, Artificial Neural Network and Genetic Algorithms for Optimization of Soybean Hydration |
| title_full | Comparative Study of Response Surface Methodology, Artificial Neural Network and Genetic Algorithms for Optimization of Soybean Hydration |
| title_fullStr | Comparative Study of Response Surface Methodology, Artificial Neural Network and Genetic Algorithms for Optimization of Soybean Hydration |
| title_full_unstemmed | Comparative Study of Response Surface Methodology, Artificial Neural Network and Genetic Algorithms for Optimization of Soybean Hydration |
| title_short | Comparative Study of Response Surface Methodology, Artificial Neural Network and Genetic Algorithms for Optimization of Soybean Hydration |
| title_sort | comparative study of response surface methodology artificial neural network and genetic algorithms for optimization of soybean hydration |
| topic | response surface methodology (RSM) artificial neural network (ANN) genetic algorithms (GA) soybean soaking |
| url | http://hrcak.srce.hr/file/74725 |
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