Prediction of denitrification capacity of alkalotolerant bacterial isolates from soil - an artificial neural network model

Olja Lj. Šovljanski, Ana M. Tomić, Lato L. Pezo, Aleksandra S. Ranitović, Siniša L. Markov

Abstract


In the past decades, the bioremediation process on the base of denitrification by aerobic heterotrophic bacteria was extensively studied for different engineering approaches. Besides the fact that only non-pathogenic and non-biofilm forming bacteria have to be used, it is very important to isolate bacteria or a group of bacteria in nature with the capacity to completely remove nitrate without accumulation of nitrogen oxides or ammonia as intermediate. In this article, the denitrification capacity of 43 bacterial strains isolated from slightly alkaline and calcite soils along the Danube River by artificial neural network (ANN) modelling were investigated. According to the obtained results, an ANN model was developed for the prediction of denitrification capacity of bacterial soil strains based on six signification denitrification indicators: biomass and N2 gas production, nitrate and nitrite concentration as well as nitrite and ammonia formation. The ANN model showed a reasonably good predictive capability of the outputs (overall R2 for prediction was 0.958). In addition, the experimental verification of the ANN in laboratory testing indicated that the ANN can predict the denitrification capacity of soil bacteria during the denitrification process in laboratory conditions.


Keywords


nitrogen cycle; denitrification kinetics; denitrifying soil bacteria; experimental verification

Full Text:

PDF (1,485 kB)

References


X. Zhu, W. Zhang, H. Chen, J. Mo, Acta Ecol. Sinica. 35 (2015) 35 (https://dx.doi.org/10.1016/j.chnaes.2015.04.004)

A. Vidaković, O. Šovljanski, D. Vučurović, G., Racić, M. Đilas, N. Ćurčić, S. Markov, CI&CEQ. 25 (2019) 403 (https://dx.doi.org/10.2298/CICEQ190111018V)

P. Ambus, S. Zechmeister-Boltenstern, Denitrification and N-Cycling in Forest Ecosystems, in Biology of the Nitrogen Cycle, H. Bothe, S.J. Ferguson, W.E. Newton, Eds, Amsterdam, Netherland, 2007, p. 343 (https://dx.doi.org/10.1016/B978-044452857-5.50023-0)

Y. Yan, D. Fu, J. Shi, Water. 11 (2019) 614 (https://dx.doi.org/10.3390/w11030614)

J. Rodziewicz, K.Ostrowska, W. Janczukowicz, A. Mielcarek, Wate. 11 (2019) 630 (https://dx.doi.org/10.3390/w11030630)

S. Casella, W.J. Payne, FEMS Microbiol. Lett. 140 (1996) 1 (https://dx.doi.org/10.1111/j.1574-6968.1996.tb08306.x)

J. Lalacut, A. Bennasar, R. Bosch, E. Garcia-Valdes, N. J. Palleroni, Microbiol. Mol. Biol. Rev.70 (2006) 510 (https://dx.doi.org/10.1128/MMBR.00047-05)

A. Rezaee, H. Godini, S. Dehestani, S. Kaviani, Iran. J. Environ. Health. Sci. Eng. 7 (2010) 313 (http://www.bioline.org.br/request?se10036)

B. Deng, L. Fu, X. Zhang, J. Zheng, L. Peng, J. Sun, H. Zhu, Y. Wang, W. Li, X. Wu, D. Wu, PLoS ONE. 9 (2014) e114886, (https://dx.doi.org/10.1371/journal.pone.0114886)

P. Bosch-Roig, J. L. Regidor Ros, R. Montes Estrellés, Int. Biodeterior. Biodegrad. 84 (2013) 266 (https://dx.doi.org/10.1016/j.ibiod.2012.09.099)

S. Vučetić, J. Ranogajec, S. Markov, A. Vidaković, H. Hiršenberger, O. Bera, Constr. Build. Mater. 142 (2017) 506 (https://dx.doi.org/10.1016/j.conbuildmat.2017.03.075)

A.G. Merma, C. A. C. Olivera, R. R. Hacha, M. L. Torem, B. F. dos Santos, J. Mater. Res. Technol. 8 (2019) 3076 (https://dx.doi.org/10.1016/j.jmrt.2019.02.022)

K. Abrougui, K. Gabsi, B. Mercatoris, C. Khemis, R. Amami, S. Chehaibi, Soil Tillage Res. 190 (2019) 202 (https://dx.doi.org/10.1016/j.still.2019.01.011)

J. S. Almeida, Curr. Opin. Biotechnol. 13 (2002), 72 (https://dx.doi.org/10.1016/S0958-1669 (02)00288-4)

O. Šovljanski, A. Tomić, L. Pezo, S. Markov, J. Sci. Food. Agric. 100 (2019) 1155 (https://dx.doi.org/10.1002/jsfa.10124)

A. M. Vidaković, O. Lj. Šovljanski, A. S. Ranitović, D. D. Cvetković, S. L. Markov, APTEFF. 48 (2017) 295 (https://dx.doi.org/10.2298/APT1748295V)

L. Bellavia, D. B. Kim-Shapiro, S. B. King, Future Sci OA. 1 (2015) 2056 (https://dx.doi.org/10.4155/fso.15.36)

Y. Zeng, L. Chen, H. Li, J. Huang, B. Yu, Adv. Mater. Res. 884 (2014) 46 (https://dx.doi.org/10.4028/www.scientific.net/AMR.884-885.46)

A. Khamparia, B. Pandey, D. Kr. Pandey, D. Gupta, A. Khanna, V. H. C. de Albuquerque, Comput. Ind. 117 (2020) in press (https://dx.doi.org/10.1016/j.compind.2020.103200)

L. Bahmani, M. Aboonajmi, A. Arabhosseini, M. Hossein, Eng. Agric. Environ. Food, 11 (2018) 25 (https://dx.doi.org/10.1016/j.eaef.2017.10.003)

B. Pavlić, L. Pezo, L. Peić Tukuljac, Z. Zeković, M. Bodroža Solarov, N. Teslić, J. Sup. Fluid. 157 (2020) in press (https://dx.doi.org/10.1016/j.supflu.2019.104687)

T. Kollo, D. von Rosen, Adv. Multivariate Stat. Matrices, Springer, Dordrecht, the Nederlands, 2005 (https://dx.doi.org/10.1007/1-4020-3419-9)

I. C. Trelea, A. L. Raoult-Wack, G. Trystram, Food Sci. Technol. Int. 3 (1997) 459 (https://dx.doi.org/10.1177/108201329700300608)

I. A. Basheer, M. Hajmeer, J. Microbiol. Meth. 43 (2000) 3 (https://dx.doi.org/10.1016/S0167-7012 (00)00201-3)

F. Dahmoune, H. Remini, S. Dairi, O. Aoun, K. Moussi, N. Bouaoudia-Madi, N. Adjeroud, N. Kadri, K. Lefsih, L. Boughani, L. Mouni, B. Nayak B, K. Madani, Ind. Crop. Product. 77 (2015) 251 (https://dx.doi.org/10.1016/j.indcrop.2015.08.0620926-6690)

Y. Yoon, G, Swales, T. M. Margavio, J. Oper. Res. Soc. 44 (2017) 51 (https://dx.doi.org/10.1057/jors.1993.6)

P. S. Madamba, LWT-Food Sci. Technol. 35 (2002) 584 (https://dx.doi.org/10.1006/fstl.2002.0914)

D. C. Montgomery, Design and analysis of experiments, John Wiley and Sons, New York, USA, 1984 (ISBN 978-1118-14692-7)

B. J. Taylor, Methods and Procedures for the Verification and Validation of Artificial Neural Networks, Springer, Berlin, Germany, 2006 (https://doi.org/10.1007/0-387-29485-6_4)

T. Turnyi, A.S. Tomlin, Analysis of Kinetics Reaction Mechanisms, Springer, Berlin, Germany, 2014 (https://doi.org/10.1007/978-3-662-44562-4)




DOI: https://doi.org/10.2298/JSC200404029S

Copyright (c) 2020 Journal of the Serbian Chemical Society

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

IMPACT FACTOR 1.097
5 Year Impact Factor 1.023
(
138 of 177 journals)