In:
Journal of the Science of Food and Agriculture, Wiley, Vol. 100, No. 3 ( 2020-02), p. 1155-1163
Abstract:
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 R 2 for temperature growth profile was 0.727, while the overall R 2 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
Type of Medium:
Online Resource
ISSN:
0022-5142
,
1097-0010
Language:
English
Publisher:
Wiley
Publication Date:
2020
detail.hit.zdb_id:
2001807-1
