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


In the past decades, the bioremediation process based on denitrification by aerobic heterotrophic bacteria was extensively studied for different engineer­ing approaches. Besides the fact that only non-pathogenic and non-biofilm form­ing bacteria must be used, it is very important to isolate bacteria or a group of bacteria in nature with the capacity to remove completely nitrate without accu­mulation of nitrogen oxides or ammonia as intermediates. In this article, the denitrification capacity of 43 bacterial strains isolated from slightly alkaline and calcite soils along the Danube River were investigated by artificial neural net­work (ANN) modelling. 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 could predict the denit­rification capacity of soil bacteria during the denitrification process in laboratory conditions.


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


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