ارزیابی کارایی مدل‌های رگرسیون چند‌متغیره و شبکۀ عصبی مصنوعی (ANN) در پیش‌بینی فعالیت آنزیم‌های آنتی‌اکسیدان در شاخسارۀ گندم نان (Triticum aestivum) تحت سمیت کادمیم

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس‌ارشد، گروه مهندسی علوم خاک، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران.

2 دانشیار گروه مهندسی علوم خاک، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران

چکیده

هدف از انجام این تحقیق مقایسة کارایی مدل‌های رگرسیون چند‌متغیره و شبکة عصبی مصنوعی (ANN) جهت پیش‌بینی مقدار فعالیت آنزیم‌های آنتی‌اکسیدان سوپراکسید دیسموتاز (SOD)، کاتالاز (CAT)، آسکوربات پراکسیداز (APX) و پراکسیداز (POX) در شاخسارة گندم (Triticumaestivum) رقم الوند در خاک آلوده به کادمیم بود. تیمارهای آزمایش شامل چهار سطح کادمیم (صفر (شاهد)، 25، 50 و 100 میلی‌گرم کادمیم در کیلوگرم خاک) بود. پس از گذشت 30 روز (هم‌زمان با مرحلة به ساقه رفتن) اقدام به برداشت نمونه‌ها و اندازه‌گیری ده پارامتر مختلف شامل وزن تر و خشک، غلظت کلروفیل‌های a و b، غلظت عناصر کادمیم، مس، آهن، منگنز، روی و پتاسیم شد. همچنین، مقدار فعالیت آنزیم‌های SOD، CAT، APX و POX اندازه‌گیری شد. در مرحلة بعد ضرایب همبستگی بین پارامترهای ده‌گانه و مقدار فعالیت آنزیم‌های آنتی‌اکسیدان تعیین شد. نتایج حاصل از مدل‌های بهینه‌شدة رگرسیون چند‌متغیره و شبکة عصبی مصنوعی نشان داد که کارایی مدل شبکة عصبی مصنوعی در پیش‌بینی مقدار فعالیت آنزیم‌های SOD و POX بیش از مدل رگرسیون چند‌متغیره بود. ضرایب همبستگی (r2) بین مقادیر اندازه‌گیری‌شده و پیش‌بینی‌شدة فعالیت آنزیم SOD برای مدل‌های رگرسیون چند‌متغیره و شبکة عصبی مصنوعی به ترتیب 76/0 و 87/0 بود. ضرایب همبستگی آنزیم POX برای مدل‌های رگرسیون چند‌متغیره و شبکة عصبی مصنوعی به ترتیب 96/0 و 98/0 بود. ضرایب همبستگی بین مقادیر اندازه‌گیری‌شده و پیش‌بینی‌شدة فعالیت آنزیم CAT برای مدل‌های رگرسیون چند‌متغیره و شبکة عصبی مصنوعی به ترتیب 97/0 و 98/0 بود. در رابطه با آنزیم APX این ضرایب برای مدل‌های رگرسیون چند‌متغیره و شبکة عصبی به ترتیب 97/0 و 99/0 بود. با توجه به نتایج این تحقیق می‌توان گفت کارایی مدل شبکة عصبی مصنوعی در پیش‌بینی مقدار فعالیت آنزیم‌های آنتی‌اکسیدان در شاخسارة رقم الوند تحت سمیت کادمیم به طور کلی بیش از مدل رگرسیون چند‌متغیره بود.     

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluation of the Performance of Multiple Regression vs Neural Network Models to Predict the Activity of Antioxidant Enzymes in Shoots of Wheat (Triticum aestivum) when under Cadmium Toxicity

نویسندگان [English]

  • Iman Javadzarrin 1
  • Babak Motesharezadeh 2
1 Graduate Student, Soil Sci. Dep. University of Tehran, Karaj, Iran
2 Associate Professor, Soil Sci. Dep. University of Tehran, Karaj, Iran
چکیده [English]

The aim followed in this study was to compare the performance of multiple regression vs neural network models to predict the activity of antioxidant enzymes Super Oxide Dismutase (SOD), CAT alase (CAT), Ascorbate Pero Xidase (APX) and PeroXidase (POX) in the shoots of wheat (Triticum aestivum), Alvand cultivar in a soil polluted with cadmium. The treatments consisted of four levels of cadmium (0 (control), 25, 50 and 100 mg kg-1 soil), respectively. After 30 days (almost simultaneous with the stage of the plant's stem elongation) plant samples were harvested. The following ten different parameters namely: wet and dry weight, chlorophyll a and b, concentrations of cadmium, copper, iron, manganese, zinc and potassium, were determined. The activities of the enzymes SOD, CAT, APX and POX were assessed. As a next step, the correlation coefficients between the ten parameters and the activity of antioxidant enzymes were determined. The results of multiple regression and neural network models optimized, showed that the efficiency of Artificial Neural Network, in predicting the activity of SOD and POX enzymes, was more pronounced than those of the Multiple Regression models. Coefficients of multiple determinations (r2) between measured and predicted values ​​of SOD activity for Multiple Regression and Neural Network models were recorded as 0.76 and 0.87 respectively. Coefficients of Multiple Determination (r2) ​​of POX activity for Multiple Regression vs Neural Network models were 0.96 and 0.98 respectively. Also the coefficients of Multiple Determination (r2) between the measured and predicted values ​​of CAT activity for multiple regression and neural network models were 0.97 and were 0.98 respectively. With regard to the APX enzyme, coefficients for Multiple Regression and Neural Network models were 0.97 and 0.99 respectively. According to the results of the research, in general the efficiency of artificial neural network model in predicting the activity of antioxidant enzymes in wheat shoots, and under toxicity of Cd was more than that of the multivariate regression model.

کلیدواژه‌ها [English]

  • Heavy metals
  • modeling
  • Soil Pollution

مراجع

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Altenbach, S.B. (2012). New insights into the effects of high temperature, drought and post-anthesis fertilizer on wheat grain development. Journal of Cereal Science 56, 39–50.
Aravind, P. Prasad, M. N. V. (2005). Cadmium–zinc interactions in a hydroponic system using Ceratophyllum demersum L.: adaptive ecophysiology, biochemistry and molecular toxicology. Brazilian Journal of Plant Physiology 17: 3–20.
Arnon DI, (1949). Copper enzymes in isolated chloroplasts, polyphenoxidase in beta vulgaris. Plant physiology 24: 1–15.
Asada, K. (1984). Chloroplasts: formation of active oxygen and its scavenging. Methods Enzymology. 105, 422-429.
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Basso, B. Ritchie, J.T. Pierce, F.J. Braga, R.P. and Jones, J.W. (2001). Spatial validation of crop models for precision agriculture. Agricultural Systems 68: 97–112.
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Bradford, M.M. (1976). A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of proteindye binding. Anal. Biochemistry. 72, 248-54.
Buszewski, B. Kowalkowski, T. (2006). A new model of heavy metal transport in the soil using non-linear artificial neural networks. Environmental Engendering Sciences. 23 (4): 589–595.
Cakmak, I. Strboe, D. and Marschner, H. (1993). Activities of hydrogen peroxide scavenging enzymes in germinating wheat seeds. Journal Experimental Botanic 44, 127-132.
Cherif, J. Mediouni, C. Ammar, W. B and Jemal, F. (2011). Interactions of zinc and cadmium toxicity in their effects on growth and in antioxidative systems in tomato plants (Solanum lycopersicum). Journal of Environmental Sciences, 23(5): 837–844.
Cho, U.H. Seo, N.H. (2005). Oxidative stress in Arabidopsis thaliana exposed to cadmium is due to hydrogen peroxide accumulation. Plant Sciences. 168, 113–120.
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Feieraband J., S. Engel, (1986). Photoinactivation of catalase in vitro and in leaves, Biochemistry Biophysics. 251: 567-576.
 
Gill, S.S. Tuteja, N. (2010). Reactive oxygen species and antioxidant machinery in abiotic stress tolerance in crop plants. Plant Physiology and Biochemistry 48, 909–930.
 
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Hema, M. Krishnamoorthy, S, (2012). Evaluation of artificial neural network and multiple regression model for Cd (II) sorption on activated carbons. Elixir Pollution 50, 10414–10419.
 
Hertwig B., P. Streb, J. Feieraband, (1992). Light dependence of catalase synthesis and degradation in leaves and the influence of interfering stress conditions, Plant Physiology. 100: 1547-1553.
 
Hsu, Y.T. Kao, C.H. ( 2007). Heat shock-mediated H2O2 accumulation and protection against Cd toxicity in rice seedlings, Plant Soil sciences 300: 137–147.
 
Jalali, M and Khanlari, Z. V. (2008). Cadmium Availability in Calcareous Soils of Agricultural Lands in Hamadan, Western Iran. Soil and Sediment Contamination, 17: 256–268.
 
Khan N. A. Samiullah, S. Singh, R. Nazar, (2007). Activities of antioxidative enzymes, sulphur assimilation, photosynthetic activity and growth of wheat (Triticum aestivum) cultivars differing in yield potential under cadmium stress, Journal Agronomy Crop Sciences. 193:  435-444.
 
Keshavarzi A. and F. Sarmadian. (2010). Comparison of Artificial Neural Network and Multivariate Regression Methods in Prediction of Soil Cation Exchange Capacity. International Journal of Environmental and Earth Sciences 1:1.
 
Koekkoek, E.J.W. Booltink, H. (1999). Neural networks models to predict soil water retention. European Journal Soil Sciences. 50: 489–495.
 
Koji Y., M. Shiro, K. Michio, T. Mitsutaka, M. Hiroshi, (2009) Antioxidant capacity and damages caused by salinity stress in apical and basal regions of rice leaf, Plant Production Sciences. 12: 319-326.
 
Liu, M, Xiangnan, L., Mi, L., Meihong, F., Wenxue Chi. (2010). Neural network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices. Biochemistry systems engineering 106, 223–233.
 
Mc Bratney, A.B. Mendoca Santos, M.L. and Minasny, B. (2003). On digital soil mapping. Geoderma 117: 3–52.
 
MacRae E.A., I.B. Ferguson, (1985). Changes in catalase activity and hydrogen peroxide concentration in plants in response to low temperature, Physiolgy Plant. 65: 51-56.
 
Mhamdi, A. Queval, G. Chaouch, S. Vanderauwera, S. Van Breusegem, F. and Noctor, G. (2010). Catalase function in plants: a focus on Arabidopsis mutants as stress-mimic models. Journal ofExperimental Botany 61: 4197–4220.
 
Milone M.T., C. Sgherri, H. Clijters, F. Navari-Izzo, (2003). Antioxidative responses of wheat treated with realistic concentrations of cadmium, Environmental Exp Botany. 50: 265-273.
 
Minasny, B. Hopmans, J.W. Harter, T. Eching, S.O. Tuli, A. and Denton, M.A. (2004). Neural networks prediction of soil hydraulic functions for alluvial soils using multistep outflow data. Soil Sciences Society. Am. J. 68: 417–429.
 
Nagamiya K., T. Motohashi, K. Nakao, S.H. Prodhan, E. Hattori, S. Hirose, K. Ozawa, Y. Ohkawa, T. Takabe, T. Takabe, A. Komamine, (2007). Enhancement of salt tolerance in transgenic rice expressing an Escherichia coli catalase gene, katE, Plant Biotechnology. 1: 49-55.
 
Pachepsky, Ya.A. Timlin, D., and Varallyay, G. (1996). Artificial neural networks to estimate soil water retention from easily measurable data. Soil Science Society of American Journal 60: 727–733.
 
Parchami, A. Ivani, R and Mashinchi M. (2011). An application of testing fuzzy hypotheses: Soil study on the bioavailability of cadmium. Scientia Iranica. 18 (3): 470–478.
 
Persson, M. Sivakumar, B. Berndtsson, R. Jacobson, O.H. and Schjonning, P. (2002). Predicting the diaelectric constant water content relationship using artificial neural networks. Soil Sciences Society. Am. J. 66: 1424–1429.
 
Polidoros N.A., J.G. Scandalios, (1999). Role of hydrogen peroxide and different classes of antioxidants in the regulation of catalase and glutathione S-transferase gene expression in maize (Zea mays L.), Physiology Plant. 106: 112-120.
 
Poluektov R.A, Topaj A.G, (2001). Crop modeling: nostalgia about present or reminiscence about future. Agronomy Journal. 93: 653–659.
 
Rabunal R. J. and Dorrado, J. (2006). Artificial neural networks in real-life applications. Published by Idea Group Publishing.
 
Sarmadian, F., R. Taghizadeh Mehrjardi A. Akbarzadeh, (2009). Modeling of some soil properties using artificial neural network and multivariate regression in Gorgan province, north of Iran”, Australian Journal of Basic and Applied Sciences., Vol. 3, No. 1, 323–329.
 
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Amini, M. K.C. Abbaspour, H. Khademi, N. Fathianpour, M. Afyuni R. Schulin. (2005). Neural network models to predict cation exchange capacity in arid regions of Iran, European Journal Soil Science., Vol. 53, 748–757.
Altenbach, S.B. (2012). New insights into the effects of high temperature, drought and post-anthesis fertilizer on wheat grain development. Journal of Cereal Science 56, 39–50.
Aravind, P. Prasad, M. N. V. (2005). Cadmium–zinc interactions in a hydroponic system using Ceratophyllum demersum L.: adaptive ecophysiology, biochemistry and molecular toxicology. Brazilian Journal of Plant Physiology 17: 3–20.
Arnon DI, (1949). Copper enzymes in isolated chloroplasts, polyphenoxidase in beta vulgaris. Plant physiology 24: 1–15.
Asada, K. (1984). Chloroplasts: formation of active oxygen and its scavenging. Methods Enzymology. 105, 422-429.
Balestrasse K.B., L. Gardey, S.M. Gallego, M.L. (2001). Tomaro, Response of antioxidant defense system in soybean nodules and roots subjected to cadmium stress, Australian Journal Plant Physiology. 28:  497-504.
Basso, B. Ritchie, J.T. Pierce, F.J. Braga, R.P. and Jones, J.W. (2001). Spatial validation of crop models for precision agriculture. Agricultural Systems 68: 97–112.
Batchelor, W.D. Yang, X.B. Tshanz, A.T. (1997). Development of a neural network for soybean rust epidemics. Transactions of the ASAE 40: 247–252.
Bolte, J. (1997). Biosystem modeling techniques. Biosystems Analysis Group, Oregon State University. (Online) Available at http://biosys.bre.orst.edu/BRE571/intro/intro_concepts.htm (verified 29th April. 2004).
Bradford, M.M. (1976). A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of proteindye binding. Anal. Biochemistry. 72, 248-54.
Buszewski, B. Kowalkowski, T. (2006). A new model of heavy metal transport in the soil using non-linear artificial neural networks. Environmental Engendering Sciences. 23 (4): 589–595.
Cakmak, I. Strboe, D. and Marschner, H. (1993). Activities of hydrogen peroxide scavenging enzymes in germinating wheat seeds. Journal Experimental Botanic 44, 127-132.
Cherif, J. Mediouni, C. Ammar, W. B and Jemal, F. (2011). Interactions of zinc and cadmium toxicity in their effects on growth and in antioxidative systems in tomato plants (Solanum lycopersicum). Journal of Environmental Sciences, 23(5): 837–844.
Cho, U.H. Seo, N.H. (2005). Oxidative stress in Arabidopsis thaliana exposed to cadmium is due to hydrogen peroxide accumulation. Plant Sciences. 168, 113–120.
De Maria, S. Rivelli, R. A. Kuffner, M. Sessitsch, A. Wenzel, W. W. Gorfer, M. Strauss, J. Puschenreiter, M. (2011). Interactions between accumulation of trace elements and macronutrients in Salix caprea after inoculation with rhizosphere microorganisms. Chemosphere, 84: 1256–1261.
Dhindsa, R.S. Dhinsa, P.P. Thorpe, T.A. (1980). Leaf senescence correlated with increased levels of membrane permeability and lipid peroxidation and decreased levels of superoxide dismutase and catalase. Journal Experimental Botanic. 32, 127-132.
 
Dudka, S. M and Piotrowska, H. T. (1996). Transfer of cadmium, lead and zinc from industrially contaminated soil to crop plants: A field study, Environmental Pollution 94:181–188.
 
Elizondo, D.A. McClendon, R.W. and Hoogenboom, G. (1994). Neural network models for predicting flowering and physiological maturity of soybean. Transactions of the ASAE 37:981–988.
Emami, A (1997). Methods of plant analysis. Volume I, Soil and Water Research Institute, Technical Bulletin No. 982.
 
Feieraband J., S. Engel, (1986). Photoinactivation of catalase in vitro and in leaves, Biochemistry Biophysics. 251: 567-576.
 
Gill, S.S. Tuteja, N. (2010). Reactive oxygen species and antioxidant machinery in abiotic stress tolerance in crop plants. Plant Physiology and Biochemistry 48, 909–930.
 
Gonçalves J F Fabiane G Antes J Maldaner L Belmonte P Luciane A Tabaldi R Rauber L Veronica R Dilson A Bisognin V Luiz D E´ rico M Moraes F And Fernando T (2009). Cadmium and mineral nutrient accumulation in potato plantlets grown under cadmium stress in two different experimental culture conditions. Plant Physiology and Biochemistry. Number 47: 814–821.
 
Gussarson, M. H. Asp, S. A. and Jensen, P. (1996). Enhancement of cadmium effects on growth and nutrient composition of Birth (Betula pendula) by buthionine sulphoximine (BSO). Experimental Botany 47: 211–215.
 
Hassan, M. J. Z. Zhu, B. and Mahmood Q. (2006). Influence of cadmium toxicity on rice genotypes as affected by zinc, sulfur and nitrogen fertilizers. Caspin Journal Environmental Sciences. 4(1): 1–8.
 
Hecht, N. R. (1987). Kolmogorov mapping, neural network existence theorem, 1st IEEE ICNN, Vol. 3, san Diego, CA.
 
Hema, M. Krishnamoorthy, S, (2012). Evaluation of artificial neural network and multiple regression model for Cd (II) sorption on activated carbons. Elixir Pollution 50, 10414–10419.
 
Hertwig B., P. Streb, J. Feieraband, (1992). Light dependence of catalase synthesis and degradation in leaves and the influence of interfering stress conditions, Plant Physiology. 100: 1547-1553.
 
Hsu, Y.T. Kao, C.H. ( 2007). Heat shock-mediated H2O2 accumulation and protection against Cd toxicity in rice seedlings, Plant Soil sciences 300: 137–147.
 
Jalali, M and Khanlari, Z. V. (2008). Cadmium Availability in Calcareous Soils of Agricultural Lands in Hamadan, Western Iran. Soil and Sediment Contamination, 17: 256–268.
 
Khan N. A. Samiullah, S. Singh, R. Nazar, (2007). Activities of antioxidative enzymes, sulphur assimilation, photosynthetic activity and growth of wheat (Triticum aestivum) cultivars differing in yield potential under cadmium stress, Journal Agronomy Crop Sciences. 193:  435-444.
 
Keshavarzi A. and F. Sarmadian. (2010). Comparison of Artificial Neural Network and Multivariate Regression Methods in Prediction of Soil Cation Exchange Capacity. International Journal of Environmental and Earth Sciences 1:1.
 
Koekkoek, E.J.W. Booltink, H. (1999). Neural networks models to predict soil water retention. European Journal Soil Sciences. 50: 489–495.
 
Koji Y., M. Shiro, K. Michio, T. Mitsutaka, M. Hiroshi, (2009) Antioxidant capacity and damages caused by salinity stress in apical and basal regions of rice leaf, Plant Production Sciences. 12: 319-326.
 
Liu, M, Xiangnan, L., Mi, L., Meihong, F., Wenxue Chi. (2010). Neural network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices. Biochemistry systems engineering 106, 223–233.
 
Mc Bratney, A.B. Mendoca Santos, M.L. and Minasny, B. (2003). On digital soil mapping. Geoderma 117: 3–52.
 
MacRae E.A., I.B. Ferguson, (1985). Changes in catalase activity and hydrogen peroxide concentration in plants in response to low temperature, Physiolgy Plant. 65: 51-56.
 
Mhamdi, A. Queval, G. Chaouch, S. Vanderauwera, S. Van Breusegem, F. and Noctor, G. (2010). Catalase function in plants: a focus on Arabidopsis mutants as stress-mimic models. Journal ofExperimental Botany 61: 4197–4220.
 
Milone M.T., C. Sgherri, H. Clijters, F. Navari-Izzo, (2003). Antioxidative responses of wheat treated with realistic concentrations of cadmium, Environmental Exp Botany. 50: 265-273.
 
Minasny, B. Hopmans, J.W. Harter, T. Eching, S.O. Tuli, A. and Denton, M.A. (2004). Neural networks prediction of soil hydraulic functions for alluvial soils using multistep outflow data. Soil Sciences Society. Am. J. 68: 417–429.
 
Nagamiya K., T. Motohashi, K. Nakao, S.H. Prodhan, E. Hattori, S. Hirose, K. Ozawa, Y. Ohkawa, T. Takabe, T. Takabe, A. Komamine, (2007). Enhancement of salt tolerance in transgenic rice expressing an Escherichia coli catalase gene, katE, Plant Biotechnology. 1: 49-55.
 
Pachepsky, Ya.A. Timlin, D., and Varallyay, G. (1996). Artificial neural networks to estimate soil water retention from easily measurable data. Soil Science Society of American Journal 60: 727–733.
 
Parchami, A. Ivani, R and Mashinchi M. (2011). An application of testing fuzzy hypotheses: Soil study on the bioavailability of cadmium. Scientia Iranica. 18 (3): 470–478.
 
Persson, M. Sivakumar, B. Berndtsson, R. Jacobson, O.H. and Schjonning, P. (2002). Predicting the diaelectric constant water content relationship using artificial neural networks. Soil Sciences Society. Am. J. 66: 1424–1429.
 
Polidoros N.A., J.G. Scandalios, (1999). Role of hydrogen peroxide and different classes of antioxidants in the regulation of catalase and glutathione S-transferase gene expression in maize (Zea mays L.), Physiology Plant. 106: 112-120.
 
Poluektov R.A, Topaj A.G, (2001). Crop modeling: nostalgia about present or reminiscence about future. Agronomy Journal. 93: 653–659.
 
Rabunal R. J. and Dorrado, J. (2006). Artificial neural networks in real-life applications. Published by Idea Group Publishing.
 
Sarmadian, F., R. Taghizadeh Mehrjardi A. Akbarzadeh, (2009). Modeling of some soil properties using artificial neural network and multivariate regression in Gorgan province, north of Iran”, Australian Journal of Basic and Applied Sciences., Vol. 3, No. 1, 323–329.
 
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