Evaluation of environmental parameters effect on the antioxidant capacity of medicinal plants

Document Type : scientific research article

Authors

1 Gorgan University of Agricultural Sciences and Natural Resources

2 Razi University of Kermanshah

Abstract

Background: Medicinal plants have wide diversity and the amounts of active compounds of them are highly variable in different areas of Iran. These changes can be depending on the plant species and environmental conditions such as temperature, precipitation and elevation or soil conditions. Therefore, evaluation the medicinal plants of an area can play an important role in understanding the potential of medicinal plants.
Materials and Methods: In this experiment, 348 plant samples were collected from 116 medicinal plants in Paveh and Ormanat. The samples were tested after identification in herbarium laboratory of Razi University of Kermanshah and were transferred to the laboratory of Department of Horticultural Sciences of Gorgan University of Agricultural Sciences and Natural Resources for antioxidant potency analysis. After preparation the herbarium samples, the plants were dried in shade as thin layer. The dried samples were employed to measure total antioxidant potential (TAOC) via TAOC method. Furthermore, climate data and soil chemical parameters were collected. To prepare the TAOC reagent, 0.6 mM sulfuric acid (H2SO4), 28 mM sodium phosphate (Na3PO4) and 4 mM ammonium molibdat (N6H24Mo7O24) were used. Finally, the relationship between them and TAOC was evaluated using regression models by data mining.
Finding: In order to assess the effect of environmental parameters on the total antioxidant content of medicinal plants in this study, the correlation between data was evaluated. The results showed that there is no correlation between environmental parameters and TAOC. In data analysis, the two-variable regression equations did not show satisfactory results, as in the stepwise method, a maximum of 31% correlation was observed, but the M5 tree regression model by dividing the data into 29 spaces provided the TAOC values which showed 91% correlation and RMSE = 16.6%.
Conclusion
The results of our study showed that, the amount of antioxidant capacity of plant based on total antioxidant had no significant correlation with environmental and soil conditions. Moreover, its multivariate relationship did not show a significant correlation, which could be due to the high diversity in the studied species. It concluded that in addition to genetic potential, total antioxidant activity of medicinal plants depends on the environmental and climatic conditions. However, understanding the potential of medicinal plants with multiple environmental and genetic parameters using data mining techniques is an important trait. Overall, the results of this study showed that,in present study the M5 decision tree method was an efficient method in evaluation of antioxidant capacity of plant.

Keywords


1.Ajith, T.A. and Janardhanan, K.K. 2007. Indian medicinal mushroom as a source of antioxidant and antitumor agents. J. Clin. Biochem. Nutr. 40: 3. 157-62.
2.Akbarinia, A., Babakhanloo, P. and Mozaffarian, V. 2006. Fluorescent Study and Biological Properties of Medicinal Plants in Qazvin Province. Iran J. Med. Art Plant. 19: 70-76. (In Persian)
3.Akbarpour, A., Khorashadizadeh, O., Shahidi, A. and Ghochanian, E. 2013. Performance evaluation of artificial neural network models in estimate production of yield saffron based on climate parameters. J. Saffron Res.1: 1. 27-35.
4.Alikhanzadeh, A. 2007. Data mining, Edition 1, publishing of computer science, Babol. (In Persian)
5.Arianfar, M., Akbari Nodehi, D., Hemmati, Kh. and Rostampour, M. 2017. Effect of height and direction on essential oil yield and some phytochemical properties of Artemisia aucheri Boiss. And Artemisia sieberi Besser. In the rangelands of South Khorasan. Rang. Sci. J. 12: 3. 281-294. (In Persian)
6.Awada, F., Kobaissi, A., Chkr, A., Hamze, K., Hayar, S. and Mortada, A. 2012. Factors affecting quantitive and qualitative variation of thyme (Origanum syriacuml.) Essential oil in Lebanon. J. Environ. Biol. 4: 1509-1514.
7.Ayse, B. 2011. Chemical Variation on the essential oil of Thymus Praecox ssp. Scorpilli Var. laniger. J. Agric. Biol.
13: 607-610.
8.Azarnoumand, H., Qavam, M., Sefidkan, F. and Tavili, A. 2009. The study of the effect of ecological characteristics (soil and height) on the quality and quality of flower and leaves of Achillea millefolium L.  Iran. J. Med. and Aroma. Plants.
4: 560-571. (In Persian)
9.Azevedo, R., Irani, P.C., Ferreira, H.D., Portes, T.A., Santos, S.C., Seraphin, J., Paula, J. and Ferri, P. 2001. chemical valiability in the essential oil of Hyptis suaveolen. J. Phytochem. 5: 733-736.
10.Babakhanzadeh Sajirani, A., Hadian, J., Abdosi, V. and Larijani, K. 2013. Investigation of some effects of different habitats on flavonoid and anthocyanin content (Echium amoenum fisch & mey) in shkhorat area of Guilan province. National Conference of Medicinal Plants. Amol. 883p. (In Persian)
11.Babakhanzadeh, A. 2010. Study of the tourist attractions of Oramanat area and its role in regional developments. Master's degree in geography and urban planning, Isfahan University. 4: 19-40. (In Persian)
12.Barreca, D., Bisignano, C., Ginestra, G., Bisignano, G., Bellocco, E., Leuzzi, U. and Gattuso, G. 2013. Polymethoxylated, C-and O-glycosyl flavonoids intangelo (Citrus reticulata× Citrus paradisi) juice and their influence on antioxidant properties. J. Food Chem. 141: 2. 1481-1488.‏
13.Bhattacharya, B. and Solomatine, D.P. 2006. Machine Learning in Sedimentation Modeling. Neural Networks J.
19: 208-214.
14.Curado, M.A., Oliveira, C.B.A.,Suzana, J.G., Jose, S.C., Pedro, S.C.and Ferri, H. 2006. Environmental Factors Influence on Chemical Polymorphism of the Essential Oils of Lychnophora ericoides. Phytochem.67: 2363-2369.
15.Davazdahemami, S. 2017. Production of Medicinal Plants Basics, Botany. Talk Publishing. Iran. 215p. (In Persian)
16.Dehghanpour, H. and Dehghanizadeh, H. 2012. Investigating the economic and social factors affecting the consumption of medicinal plants in Yazd from the people's point of view. Iran J. Med. Art. Plant. 30: 1. 57-67. (In Persian)
17.Etemad-Shahidi, A. and Mahjoobi, J. 2009. Comparison between M5΄ Model Tree and Neural Networks for prediction of significant wave height. IEEE J. Ocean. Eng. 36: 15-16. 1175-1181.
18.Fallahi, M., Varvani, H. and Golian,S. 2012. Forcast precipitation using regression tree for flood control.5th conf watershed and water resource managemene and land. Kerman.(In Persian)
19.Franz, Ch. 1983. Nutrient and water management for medicinal and aromatic plants. Acta Hort. 132: 203-215.
20.Fujii, H. and Zhu, J.K. 2009. An autophosphorylation site of the protein kinase SOS2 is important for salt tolerance in Arabidopsis. Mol Plant.2: 1. 183-190.‏
21.Hosseini, S.M.R., Ganji Khoram Del, N. and Farahani, A.H. 2016. Estimating daily evapotranspiration by M5 decision tree and artificial neural network. J. Appl. Res. Water Sci. 3: 2. 35-44.(In Persian)
22.Hraš, A.R., Hadolin, M., Knez, Ž. and Bauman, D. 2000. Comparison of antioxidative and synergistic effects of rosemary extract with α-tocopherol, ascorbyl palmitate and citric acidin sunflower oil. Food Chem. X.71: 2. 229-233.‏
23.Ismaili Gisavandani, H., Akhundali, M.h., Zarei, H. and Taghiyan, M. 2017. Regional flood analysis by comparing models of M5 decision tree algorithm and regression. J. Irrig. Sci. Eng.40: 4. 183-195. (In Persian)
24.Jamshidi, A., Aminzadeh, M., Azarniwand, H. and Abedi, M. 2005. Influence of altitude on the quantity and quality of essential oil of Thymus kotschyanus. J. Med. Plant. 18: 86-93. (In Persian)
25.Kaul, M., Hill, R.L. and Walthall, C. 2005. Artificial neural networks for corn and soybean yield prediction. Agric. Syst. 85: 1-18.
26.Khosropour, B., Ceiahposh, A. and Karbalaei, Z. 2015. Cultivation of medicinal plants and the production of agricultural products. Med. Plant. Herbal Med. Symp. 1: 1-7. (In Persian)
27.Kisi, O. and Kilic, Y. 2016. An investigation on generalization abilityof artificial neural networks andM5 model tree in modeling reference evapotranspiration. Theor. Appl. Climatol. 126: 3-4. 413-425.‏
28.Li, B., Wei, A., Song, C., Li, N. and Zhang, J. 2008. Heterologous expression of the TsVP gene improves the drought resistance of maize. Plant Biotechnol. J. 6: 2. 146-159.‏
29.López-Pérez, L., del Carmen Martínez-Ballesta, M., Maurel, C. and Carvajal, M. 2009. Changes in plasma membrane lipids, aquaporins and proton pump of broccoli roots, as an adaptation mechanism to salinity. Phytochem.70: 4. 492-500.‏
30.Mahmoudzadeh Tilami, Z. 2014. Effect of some ecological factors on the quantity and quality of essential oil of Marrubium vulgare in Chahar Bagh rangelands of Golestan province. Master thesis. Gonbad Kavous University. Agric. Nat. Res. Pp: 4-25. (In Persian)
31.Mohammad Nejad Ganji, S.M., Moradi, H., Ghanbari, A. and Akbarzadeh, M. 2014. Investigating the effect of height on quantity and quality of essential oils of Rosmarinus officinalis L. cultivated in two regions of Mazandaran province. Jecophytochem. Med. Plant. 2: 5. 36-42. (In Farsi)
32.Moharram, H.A. and Youssef, M.M. 2014. Methods for determining the antioxidant activity: a review. Alex. J. Fd. Sci. Technol. 11: 1. 31-42.‏
33.Nahrein, F., Sattari, M. and Salmasi, F. 2013. Predicting Energy Dissipation in Flowing Chiffon Stepping Stone Using a M5 Model Tree, J. Water Res. Engin.6: 16. 75-86.
34.O’sullivan, A.M., O’Callaghan, Y.C., O’Grady, M.N., Queguineur, B., Hanniffy, D., Troy, D.J. and O’Brien, N.M. 2011. In vitro and cellular antioxidant activities of seaweed extracts prepared from five brown seaweeds harvested in spring from the west coast of Ireland. Food Chem. X. 126: 3. 1064-1070.‏
35.Omidbaigi, R. 2005. Production and manufacturing the herbs. Tehran Univ. Iran. 1: 347. (In Persian)
36.Ozra, S., Ahmadi, A., Zeinali, A. and Parsa, M. 2014. Comparison of the content of phenolic compounds, flavonoids and antioxidant activity of the two species of Scutellaria pinnatifida in northern Iran. J. Rafsenjan Univ. Med. Sci. 13: 3. 249-266. (In Persian)
37.Page, A.L., Miller, R.H. and Keeney, D.R. 1992. Method of soil Analysis. Part II: Chemical and Mineralogical Properties (Second Edition ed.). Madison, Wisconsin: SSSA.
38.Pal, M. 2006. M5 model tree for land cover classification. Int. J. Rem. Sens. 27: 4. 825-831.
39.Qasemi, A. 2009. Medicinal and aromatic plants (their identification and study). Islam Azad Uni Shahr-e Kord Public. Iran. 542p. (In Persian)
40.Quinlan, J.R. 1992. Learning with Continuous Classes. Proceedings of AI’92, World Sci. Pp: 343-348.
41.Rasti, A., Sefidkon, F. and Jaimand, K. 2001. Effect of habitat, eleviation, aspect and slope on the quality and quantity of essential oil of Juniperus sp., in the Amarlooi Roodbar regions. Inter. Conf. Med. Plant. 159p. (In Persian)
42.Richard, L.A. 1969. Diagnosis and improvements of saline and alkali soils. Agric, Handbook No. 60. USDA, WA. DC.
43.Rodríguez-Milla, M.A. and Salinas, J. 2009. Prefoldins 3 and 5 play an essential role in Arabidopsis tolerance to salt stress. Mol. Plant. 2: 3. 526-534.‏
44.Ruminska, A. 1978. The Influence of fertilizers on the content of active compounds in spice crop and medicinal plants. Acta Hort. 73: 143-164.
45.Saburifard, A., Qhasemnejad, A., Hemmati, Kh., Hezarjaribi, A., Bahrami, M.R. and Nosrati, F. 2008. Estimation of biomass performance of satureja hortensis L. using soil parameters and artificial neural network. Haryana J. Hort. Sci. 31: 3. 448-456.
46.Saburifard, H., Qasemnejad, A., Hemmati, Kh., Hezarjaribi, A. and Bahrami, M.R. 2019. Evaluation of the Effectiveness of Artificial Neural Network Models in Estimating Yieldof Satureja hortensis L. Oil Basedon Soil Properties. Aust. J. Crop Sci.
26: 2. 47-58.
47.Sattari, M.T., Rezazadeh Joodi, A., Safdari, F. and Ghahramanian, F. 2016. Evaluation of the performance of tree model models M5 and support vector regression in suspended sediment modeling of the river. J. Soil Water Cons. 6: 1. 109-124. (In Persian)
48.Seo, P.J., Xiang, F., Qiao, M., Park, J.Y., Lee, Y.N., Kim, S.G. andPark, C.M. 2009. The MYB96 transcription factor mediates abscisic acid signaling during drought stress response in Arabidopsis. J. Plant Physiol. 151: 1. 275-289.‏
49.Sharifiyan, H. and Ghorbani, K.H.2014 Improve the estimation ofpotential evapotranspiration using the correction coefficient using the model M5 decision tree. J. Irrig. Drain.8: 1. 53-61. (In Farsi)
50.Sherwin, E.R. 1990. Antioxidants. In: Food Additives. Branen R. (ed.), New York: Marcel Dekker. Pp: 139-193.
51.Shun, Y.M., Wen, Y.H., Yong, C.Y. and Jian, G.S. 2003. Two benzyl dihydroflavones from phellinus igniarius. Chin. Chem. Lett. 14: 8. 810-13.
52.Song, H., Zhang, Q., Zhang, Z. and Wang, J. 2010. In vitro antioxidant activity of polysaccharides extracted from Bryopsis plumosa. Carbohydrate Polymers. 80: 4. 1057-1061.‏
53.Sun, L., Zhang, J., Lu, X., Zhang, L. and Zhang, Y. 2011. Evaluation to the antioxidant activity of total flavonoids extract from persimmon leaves. Food Chem Toxicol. 49: 2689-2696.
54.Taheri, A., Ghaffari, M., Bagherpour, N.S. and Ata Ron Freiman, G. 2017. Study of antioxidant properties of Cystoseira trinodis extracts from Chabahar beaches. Month. J. Shahid Sadoughi Univ. Med. Sci. Yazd,25: 8. 658-669. (In Persian)
55.Tavousi, T., Rahimi, D. and Khosravi, M. 2014. Locating suitable ecotourism zones. Case Study: Oramanat Area. Geography Space Magazine. J. Golestan Univ. 4: 20-40. (In Persian)
56.Tepe, B., Daferera, D., Sokmen, A., Sokmen, M. and Polissiou, M. 2005. Antimicrobial and antioxidant activities of the essential oil and various extracts of Salvia tomentosa Miller (Lamiaceae). Food Chem. X. 90: 3. 333-340.‏
57.Terzi, O. 2007. Data mining approach for estimation evaporation from free water surface. J. Appl. Sci. 7: 4. 593-596.‏
58.Thanonkaew, A., Benjakul, S., Visessanguan, W. and Decker, E.A. 2008. The effect of antioxidants on the quality changes of cuttlefish (Sepia pharaonis) muscle during frozen storage. LWT-Food Science and Technology. 41: 1. 161-169.‏
59.Two Crows Corporation. 1999. Introduction to data mining and knowledge discovery, third ed., Postmac, MD. Available at: www.twocrows.com (April 29, 2000).
60.Verdinejad, V., Shabniyavasl, M.and Basharat, S. 2016. Modeling evapotranspiration using linear, nonlinear regression and artificial neural network in greenhouse (reference study of reference plant, cucumber and tomato). J. Water Soil (Agric. Sci. Technol.). 30: 5. 1334-1346.
61.Wang, Y. and Witten, I.H. 1997. Induction of model trees for predicting continuous classes. Proc.Europ Conf. Machine Learning, Univ. Econ. Info Statistics, Prague.
62.Wink, N. and Ktarey, D.B. 1994. Variability of quimolividime alcoholide profile of Lupimus argentinus (sabaceae) North America. J. Syst. Ecol. 22: 7. 663-669.
63.Yahyapour, R. and Rahdari, P. 2013. Impact of soil physical and chemical properties on the quantity and quality of plant alkaloids (Berberis vulgaris L.). Nat. Conf. Med. Plant. 889p. (In Persian)
64.Zahiri, J. 2015. Application of nonparametric models of CART andM5 'in calculating the scour depth around bridges. J. Irrig. Water Engin.5: 20. 35-50. (In Persian)
65.Zhang, L., Tian, L.H., Zhao, J.F., Song, Y., Zhang, C.J. and Guo, Y. 2009. Identification of an apoplastic protein involved in the initial phase of salt stress response in rice root by two-dimensional electrophoresis. J. Plant Physiol. 149: 2. 916-928.