Journal of Nuclear Science, Engineering and Technology (JONSAT)
In cooperation with the Iranian Nuclear Society

Evaluation of gamma ray effect on wheat bakery properties in Omid, Roshan and Tabasai cultivars by artificial neural network

Document Type : Research Paper

Authors

M.R Rahemi
A Yamchi
S Navabpour
H Soltanloo
P Roepstorff
https://doi.org/10.24200/nst.2020.1078
Abstract
The genome of a plant is the most critical factor to control bakery- quality trait in wheat, where it can bemade by applying genetic variation upon using mutagens for its improvement. In this study, chemical and Farinograph experiments were investigated in T-66-58-60, O-64-1-10, RO-1, RO-3 and RO-5 lines from Tabassi, Omid, and Roshan cultivar, respectively. Also, the sigmoid transfer function was used for the assessment of factors by the model of feed-forward neural network with training method of levenberg-Marquardt algorithm. The chemical traits of Zeleny number, the hardness, wet gluten and protein content in the RO-3 line increased significantly compared with the control. Also, water absorption percentage and valorimeter value increased substantially in the O-64-1-10, whereas it was shown that the dough softening after 10 and 20 minutes decreased significantly compared with the control. The protein content, bread volume, Farinograph quality number and E10 properties had the most significant impact on the neural network model. The results show a positive effect of the irradiation on the improvement of dough quality properties.

Highlights

1. S.F. Majd, M.R. Ardekani, Nuclear techniques in agriculture, Tehran University Press, (2009) (In Persian).

2. L.M. Corpuz, E.G. Heyne, G.M. Paulsen, Increasing grain protein content of hard red winter wheat (Triticum aestivum L.) by mutation breeding, Theoretical and Applied Genetics, 65, 41-46 (1983).

3. M. Mangova, G. Rachovska, Technological characteristics of newly developed mutant common winter wheat lines, Plant Soil Environment, 50, 84–87 (2004).

4. M.S. Swaminathan, Role of mutation breeding in a changing agriculture. Induced mutations in plants, In: Proc Symposium of IAEA-FAO, Pullman. IAEA, Vienna, 719-734 (1969).

5. N.O. Kozub, et al, Study of the effects of gamma-irradiation of common wheat F1 seeds using gliadins as genetic markers, Tsitol Genetic, 47, 17-25 (2013).

6. S.M. Mousavi, et al, Modelling and optimization of Mn/activate carbon nanocatalysts for NO reduction: comparison of RSM and ANN techniques, Environmental technology, 34, 1377-1384 (2013).

7. M. Safa, S. Samarasinghe, M. Nejat, Prediction of Wheat Production Using Artificial Neural Networks and Investigating Indirect Factors Affecting It: Case Study in Canterbury Province, New Zealand. Journal of Agricultural Science and Technology, 17, 791-803 (2015).

8. R. Alvarez, Predicting Average Regional Yield and Production of Wheat in the Argentine Pampas by an Artificial Neural Network Approach, European Journal Agronomy, 30, 70-77 (2009).

 

9. M. Faramarzi, et al, Modeling Wheat Yield and Crop Water Productivity in Iran: Implications of Agricultural Water Management for Wheat Production, Agriculture Water Management. 97, 1861-1875 (2010).

10. M. Özdoğan, Modeling the Impacts of Climate Change on Wheat Yields in Northwestern Turkey, Agriculture Ecosystem Environment, 141, 1-12 (2011).

11. C. Folberth, et al, Impact of Input Data Resolution and Extent of Harvested Areas on Crop Yield Estimates in Large scale Agricultural Modeling for Maize in the USA, Ecological Modelling, 235–236, 8-18 (2012).

12. AACC, Approved Methods of the American Association of Cereal Chemists8th Edition, St Paul, Minessot, (1990).

13. Anonymous, Instruction manual, Farinograph -E, Publication No 17073.5E. Brabender Gesellschaft mit beschränkter Haftung, 1-56 (2006).

14. A.R. Khataee. M.B. Kasiri, Artificial neural networks modeling of contaminated water treatment processes by homogeneous and heterogeneous Nano-catalysis. Journal Molecular, Catalysis A: Chemical, 331, 86–100 (2010).

15. F. Shahinnia, A. Rezaie, A. Saedi, Variation and path coefficient analysis of bread making quality traits in breeding lines, cultivars and landrace varieties of wheat, Journal of Science and Technology of Agricultural and Natural Resources, 6, 77-88 (2002).

 

 

16. A. Arzani, Grain quality of durum wheat germplasm as affected by heat and drought stress at grain filling period, Wheat Information Service, 94, 9-14 (2002).

17. M. Rajabi Hashjin, et al, Evaluating the Cooking Quality Traits in Bread and Durum Wheat, Crop Biotech, 4, 33-41 (2013) (In Persian).

 

18. H. Ahmadi-Gavligi, et al, Protein content of important wheat varieties in Iran and their technological properties, Food Science and Technology, 2, 1-7 (2004) (In Persian).

19. D.B. Fowler, J. Brydon, I.A. Delaroche, Environmental and genotype influence on grain protein concentration of wheat and rye, Agronomy Journal, 82, 655-664 (1990).

20. D.B. Fowler, I.A. Delaroche, Wheat quality evaluation: Influence of genotype and environment. Canadian Journal of Plant Science, 55, 263-269 (1975).

21. A.C.S. Rao, et al, Cultivar and climatic effects on the protein content of soft white winter wheat, Crop Science, 85, 1023-1082 (1993).

22. G. Najafian, et al, Bread making quality attributes of Iranian trade cultivars of wheat and their HMW glutenin subunits composition, In: Proceedings of 11 International, Wheat Genetics Symposium, (2008).

23. F. Balestra, Empirical and fundamental mechanical tests in the evaluation of dought and bread rheological properties, Alma master Studiorum University Dibologna, 1-169 (2009).

24. M. Akbari Rad, et al, Study of genetic variation in baking quality related characteristics in bread wheat advanced lines and commercial cultivars, Iranian Journal of Crop Sciences, 12, 213-226 (2010) (In Persian).

25. R.B. Gupta, et al, Accumulation of protein subunits and their polymers in developing grains of hexaploid wheat, Experimental Botany, 47, 1377-1385 (1996).

26. H. Abbasi, M. Mohammadifar, Prediction of Fundamental Rheological Properties of Dough with Artificial Neural Networks-Genetic Algorithm. Iranian Journal of Nutrition Sciences & Food Technology, 10, 67-77 (2015).

27. Y. Horimoto, et al, Neural Networks vs Principal Component Regression for Prediction of Wheat Flour Loaf Volume in Baking Tests. Journal Food Science, 60,  429-433 (1995).

28. E. Razmi-Rad, Prediction of rheological properties of Iranian bread dough from chemical composition of wheat flour by using artificial neural networks. Journal of Food Engineering, 81, 728-734 (2007).

29. A.C. Mutlu, et al, Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks. European food research and technology, 233,267-274 (2011).

Keywords

Wheat
Gamma ray
Mutant
Artificial neural network
1. S.F. Majd, M.R. Ardekani, Nuclear techniques in agriculture, Tehran University Press, (2009) (In Persian).
2. L.M. Corpuz, E.G. Heyne, G.M. Paulsen, Increasing grain protein content of hard red winter wheat (Triticum aestivum L.) by mutation breeding, Theoretical and Applied Genetics, 65, 41-46 (1983).
3. M. Mangova, G. Rachovska, Technological characteristics of newly developed mutant common winter wheat lines, Plant Soil Environment, 50, 84–87 (2004).
4. M.S. Swaminathan, Role of mutation breeding in a changing agriculture. Induced mutations in plants, In: Proc Symposium of IAEA-FAO, Pullman. IAEA, Vienna, 719-734 (1969).
5. N.O. Kozub, et al, Study of the effects of gamma-irradiation of common wheat F1 seeds using gliadins as genetic markers, Tsitol Genetic, 47, 17-25 (2013).
6. S.M. Mousavi, et al, Modelling and optimization of Mn/activate carbon nanocatalysts for NO reduction: comparison of RSM and ANN techniques, Environmental technology, 34, 1377-1384 (2013).
7. M. Safa, S. Samarasinghe, M. Nejat, Prediction of Wheat Production Using Artificial Neural Networks and Investigating Indirect Factors Affecting It: Case Study in Canterbury Province, New Zealand. Journal of Agricultural Science and Technology, 17, 791-803 (2015).
8. R. Alvarez, Predicting Average Regional Yield and Production of Wheat in the Argentine Pampas by an Artificial Neural Network Approach, European Journal Agronomy, 30, 70-77 (2009).
 
9. M. Faramarzi, et al, Modeling Wheat Yield and Crop Water Productivity in Iran: Implications of Agricultural Water Management for Wheat Production, Agriculture Water Management. 97, 1861-1875 (2010).
10. M. Özdoğan, Modeling the Impacts of Climate Change on Wheat Yields in Northwestern Turkey, Agriculture Ecosystem Environment, 141, 1-12 (2011).
11. C. Folberth, et al, Impact of Input Data Resolution and Extent of Harvested Areas on Crop Yield Estimates in Large scale Agricultural Modeling for Maize in the USA, Ecological Modelling, 235–236, 8-18 (2012).
12. AACC, Approved Methods of the American Association of Cereal Chemists8th Edition, St Paul, Minessot, (1990).
13. Anonymous, Instruction manual, Farinograph -E, Publication No 17073.5E. Brabender Gesellschaft mit beschränkter Haftung, 1-56 (2006).
14. A.R. Khataee. M.B. Kasiri, Artificial neural networks modeling of contaminated water treatment processes by homogeneous and heterogeneous Nano-catalysis. Journal Molecular, Catalysis A: Chemical, 331, 86–100 (2010).
15. F. Shahinnia, A. Rezaie, A. Saedi, Variation and path coefficient analysis of bread making quality traits in breeding lines, cultivars and landrace varieties of wheat, Journal of Science and Technology of Agricultural and Natural Resources, 6, 77-88 (2002).
 
 
16. A. Arzani, Grain quality of durum wheat germplasm as affected by heat and drought stress at grain filling period, Wheat Information Service, 94, 9-14 (2002).
17. M. Rajabi Hashjin, et al, Evaluating the Cooking Quality Traits in Bread and Durum Wheat, Crop Biotech, 4, 33-41 (2013) (In Persian).
 
18. H. Ahmadi-Gavligi, et al, Protein content of important wheat varieties in Iran and their technological properties, Food Science and Technology, 2, 1-7 (2004) (In Persian).
19. D.B. Fowler, J. Brydon, I.A. Delaroche, Environmental and genotype influence on grain protein concentration of wheat and rye, Agronomy Journal, 82, 655-664 (1990).
20. D.B. Fowler, I.A. Delaroche, Wheat quality evaluation: Influence of genotype and environment. Canadian Journal of Plant Science, 55, 263-269 (1975).
21. A.C.S. Rao, et al, Cultivar and climatic effects on the protein content of soft white winter wheat, Crop Science, 85, 1023-1082 (1993).
22. G. Najafian, et al, Bread making quality attributes of Iranian trade cultivars of wheat and their HMW glutenin subunits composition, In: Proceedings of 11 International, Wheat Genetics Symposium, (2008).
23. F. Balestra, Empirical and fundamental mechanical tests in the evaluation of dought and bread rheological properties, Alma master Studiorum University Dibologna, 1-169 (2009).
24. M. Akbari Rad, et al, Study of genetic variation in baking quality related characteristics in bread wheat advanced lines and commercial cultivars, Iranian Journal of Crop Sciences, 12, 213-226 (2010) (In Persian).
25. R.B. Gupta, et al, Accumulation of protein subunits and their polymers in developing grains of hexaploid wheat, Experimental Botany, 47, 1377-1385 (1996).
26. H. Abbasi, M. Mohammadifar, Prediction of Fundamental Rheological Properties of Dough with Artificial Neural Networks-Genetic Algorithm. Iranian Journal of Nutrition Sciences & Food Technology, 10, 67-77 (2015).
27. Y. Horimoto, et al, Neural Networks vs Principal Component Regression for Prediction of Wheat Flour Loaf Volume in Baking Tests. Journal Food Science, 60,  429-433 (1995).
28. E. Razmi-Rad, Prediction of rheological properties of Iranian bread dough from chemical composition of wheat flour by using artificial neural networks. Journal of Food Engineering, 81, 728-734 (2007).
29. A.C. Mutlu, et al, Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks. European food research and technology, 233,267-274 (2011).
Journal of Nuclear Science, Engineering and Technology (JONSAT)
Volume 40, Issue 4 - Serial Number 90
February 2020
Pages 119-127

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