Comparison of Trial and Error and Genetic Algorithm in Neural Network Development for Estimating Farinograph Properties of Wheat-flour Dough
Background and Objectives: Rheological characteristics of dough are important for achieving useful information about raw-material quality, dough behavior during mechanical handling, and textural characteristics of products. Our purpose in the present research is to apply soft computation tools for p...
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| Format: | Article |
| Language: | English |
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Shahid Beheshti University of Medical Sciences
2015-06-01
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| Series: | Nutrition and Food Sciences Research |
| Subjects: | |
| Online Access: | http://nfsr.sbmu.ac.ir/browse.php?a_code=A-10-420-1&slc_lang=en&sid=1 |
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| author | Hajar Abbasi Seyyed Mahdi Seyedain Ardabili Mohammad Amin Mohammadifar Zahra Emam-Djomeh |
| author_facet | Hajar Abbasi Seyyed Mahdi Seyedain Ardabili Mohammad Amin Mohammadifar Zahra Emam-Djomeh |
| author_sort | Hajar Abbasi |
| collection | DOAJ |
| description | Background and Objectives: Rheological characteristics of dough are important for achieving useful information about raw-material quality, dough behavior during mechanical handling, and textural characteristics of products. Our purpose in the present research is to apply soft computation tools for predicting the rheological properties of dough out of simple measurable factors.
Materials and Methods: One hundred samples of white flour were collected from different provinces of Iran. Seven physic-chemical properties of flour and Farinogram parameters of dough were selected as neural network’s inputs and outputs, respectively. Trial-and-error and genetic algorithm (GA) were applied for developing an artificial neural network (ANN) with an optimized structure. Feed-forward neural networks with a back-propagation learning algorithm were employed. Sensitivity analyses were conducted to explore the ability of inputs in changing the Farinograph properties of dough.
Results: The optimal neural network is an ANN-GA that evolves a four-layer network with eight nodes in the first hidden layer and seven neurons in the second hidden layer. The average of normalized mean square error, mean absolute error and correlation coefficient in estimating the test data set was 0.222, 0.124 and 0.953, respectively. According to the results of sensitivity analysis, gluten index was selected as the most important physicochemical parameter of flour in characterization of dough’s Farinograph properties.
Conclusions: An ANN is a powerful method for predicting the Farinograph properties of dough. Taking advantages of performance criteria proved that the GA is more powerful than trial-and-error in determining the critical parameters of ANN’s structure, and improving its performance.
Keywords: Artificial neural network, Genetic algorithm, Rheological characterization, Wheat-flour dough |
| first_indexed | 2024-12-22T19:39:34Z |
| format | Article |
| id | doaj.art-fcbffd5d83c04675893fae7778834761 |
| institution | Directory Open Access Journal |
| issn | 2283-0441 2383-3009 |
| language | English |
| last_indexed | 2024-12-22T19:39:34Z |
| publishDate | 2015-06-01 |
| publisher | Shahid Beheshti University of Medical Sciences |
| record_format | Article |
| series | Nutrition and Food Sciences Research |
| spelling | doaj.art-fcbffd5d83c04675893fae77788347612022-12-21T18:14:54ZengShahid Beheshti University of Medical SciencesNutrition and Food Sciences Research2283-04412383-30092015-06-01232938Comparison of Trial and Error and Genetic Algorithm in Neural Network Development for Estimating Farinograph Properties of Wheat-flour DoughHajar Abbasi0Seyyed Mahdi Seyedain Ardabili1Mohammad Amin Mohammadifar2Zahra Emam-Djomeh3 Department of Food Science and Technology, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran Department of Food Science and Technology, Faculty of Agriculture and Natural Resources, Science and Research Branch, Islamic Azad University, Tehran, Iran Department of Food Science and Technology, Faculty of Nutrition Sciences, Food Science and Technology / National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, PO Box 19395-47471, Tehran, Iran Transfer Phenomena Laboratory, Department of Food Science, Technology and Engineering, Faculty of Agricultural Engineering and Technology, Agricultural Campus of the University of Tehran, PO Box 4111, 31587-11167 Karadj, Iran Background and Objectives: Rheological characteristics of dough are important for achieving useful information about raw-material quality, dough behavior during mechanical handling, and textural characteristics of products. Our purpose in the present research is to apply soft computation tools for predicting the rheological properties of dough out of simple measurable factors. Materials and Methods: One hundred samples of white flour were collected from different provinces of Iran. Seven physic-chemical properties of flour and Farinogram parameters of dough were selected as neural network’s inputs and outputs, respectively. Trial-and-error and genetic algorithm (GA) were applied for developing an artificial neural network (ANN) with an optimized structure. Feed-forward neural networks with a back-propagation learning algorithm were employed. Sensitivity analyses were conducted to explore the ability of inputs in changing the Farinograph properties of dough. Results: The optimal neural network is an ANN-GA that evolves a four-layer network with eight nodes in the first hidden layer and seven neurons in the second hidden layer. The average of normalized mean square error, mean absolute error and correlation coefficient in estimating the test data set was 0.222, 0.124 and 0.953, respectively. According to the results of sensitivity analysis, gluten index was selected as the most important physicochemical parameter of flour in characterization of dough’s Farinograph properties. Conclusions: An ANN is a powerful method for predicting the Farinograph properties of dough. Taking advantages of performance criteria proved that the GA is more powerful than trial-and-error in determining the critical parameters of ANN’s structure, and improving its performance. Keywords: Artificial neural network, Genetic algorithm, Rheological characterization, Wheat-flour doughhttp://nfsr.sbmu.ac.ir/browse.php?a_code=A-10-420-1&slc_lang=en&sid=1Artificial neural network Genetic algorithm Rheological characterization Wheat-flour dough |
| spellingShingle | Hajar Abbasi Seyyed Mahdi Seyedain Ardabili Mohammad Amin Mohammadifar Zahra Emam-Djomeh Comparison of Trial and Error and Genetic Algorithm in Neural Network Development for Estimating Farinograph Properties of Wheat-flour Dough Nutrition and Food Sciences Research Artificial neural network Genetic algorithm Rheological characterization Wheat-flour dough |
| title | Comparison of Trial and Error and Genetic Algorithm in Neural Network Development for Estimating Farinograph Properties of Wheat-flour Dough |
| title_full | Comparison of Trial and Error and Genetic Algorithm in Neural Network Development for Estimating Farinograph Properties of Wheat-flour Dough |
| title_fullStr | Comparison of Trial and Error and Genetic Algorithm in Neural Network Development for Estimating Farinograph Properties of Wheat-flour Dough |
| title_full_unstemmed | Comparison of Trial and Error and Genetic Algorithm in Neural Network Development for Estimating Farinograph Properties of Wheat-flour Dough |
| title_short | Comparison of Trial and Error and Genetic Algorithm in Neural Network Development for Estimating Farinograph Properties of Wheat-flour Dough |
| title_sort | comparison of trial and error and genetic algorithm in neural network development for estimating farinograph properties of wheat flour dough |
| topic | Artificial neural network Genetic algorithm Rheological characterization Wheat-flour dough |
| url | http://nfsr.sbmu.ac.ir/browse.php?a_code=A-10-420-1&slc_lang=en&sid=1 |
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