Temperature and pH growth profile prediction of newly isolated bacterial strains from alkaline soils

• Published in: Journal of the science of food and agriculture 2020-02, Vol.100 (3), p.1155-1163
• Main Authors: Šovljanski, Olja, Tomić, Ana, Pezo, Lato, Markov, Siniša
• Format: Article
• Language: English
• Subjects: Adaptability; Alkaline soils; alkaliphilic sporogenic bacteria; Artificial neural networks; Bacteria; Bacteria - classification; Bacteria - genetics; Bacteria - growth & development; Bacteria - isolation & purification; bacterial isolation; Biodiversity; Bioremediation; Environmental conditions; Extreme environments; Hydrogen-Ion Concentration; Industrial applications; Laboratories; Limestone; Mapping; mathematical modelling; Mathematical models; Microbial activity; Microbiology; Microbiota; Microorganisms; Models, Theoretical; Mountains; Neural networks; Organic chemistry; pH effects; Predictions; Rivers; Soil - chemistry; Soil bacteria; Soil investigations; Soil Microbiology; Soil microorganisms; Soils; Strains (organisms); Temperature; Waste Water - analysis; Waste Water - microbiology; Wastewater
• ISSN: 0022-5142; 1097-0010
• DOI: 10.1002/jsfa.10124

BACKGROUND Soil microorganisms can form complex and varied communities which interact with each other in many different ways depending on environmental conditions. These microbial diversities are accompanied by different metabolic paths and adaptability reflected even in extreme environments. In recent decades, the biodiversity of microbes in extreme environments has been in scientific focus because such specifically adapted bacteria can improve bioremediation processes in industrial and agricultural applications. Instead of the time‐consuming process of identification of new bacterial strains from habitats rich in microbiota, artificial neural networks have been proposed as a mapping model for resolving the problem of prediction of microbial behaviour. RESULTS The occurrence and diversity of alkaliphilic sporogenic bacteria in alkaline soils were investigated. For this purpose, soil samples were collected from various locations: leached soil from the Danube river, cement factory wastewater accumulation, deposit of limestone near the Bešenovo lake and the Beli Majdan cave in the Fruška gora mountain. According to the obtained results, two empirical models were developed that gave a good fit to experimental data and were able to predict successfully the pH and temperature growth profiles of the natural isolates. The artificial neural network models showed a reasonably good predictive capability (overall R2 for temperature growth profile was 0.727, while the overall R2 for pH growth profile was 0.906). CONCLUSIONS The developed mathematical models provided adequate precision for practical study in the microbiology laboratory and scale‐up processes for a wide range of laboratory and industrial applications, where specifically adapted microbial communities are needed. © 2019 Society of Chemical Industry