Non-Structured Kinetic Model of Aspergillus niger Growth and Substrate Uptake in a Batch Submerged Culture

Ardestani, Fatemeh and Kasebkar, Roxana (2014) Non-Structured Kinetic Model of Aspergillus niger Growth and Substrate Uptake in a Batch Submerged Culture. British Biotechnology Journal, 4 (9). pp. 970-979. ISSN 22312927

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Abstract

Aims: To investigate cell growth profile of Aspergillus niger in a batch submerged culture medium and then evaluation of cell kinetic behavior using some different non-structured kinetic models.

Methodology: Experiments of cell growth and substrate utilization were conducted in batch submerged cultures with identified medium composition. Fitness assessment of experimental data on the cell growth and glucose consumption by models was performed using the curve-fitting tool in Mat Lab software. This report is the first in the kinetic investigation of Aspergillus niger PTCC 5010 with the studied models. This work was performed in Department of Chemical Engineering, Islamic Azad University, Qaemshahr Branch between April 2013 to September 2013.

Results: Based on the obtained results; Moser kinetic model with R2 equal to 0.913 and Gompertz kinetic model with R2 equal to 0.949 were the best fitted models to describe the growth behavior of Aspergillus niger PTCC 5010 in the applied culture condition. Maximum specific cell growth rate with Moser and Gompertz kinetic models were 0.024 and 0.003h-1, respectively. Other kinetic constants for all studied models were also determined at the applied culture conditions. The consistency of the experimental data with Monod, Verhulst and Contois kinetic models wasn't in an acceptable range.

Conclusion: In scale up biochemical projects by Aspergillus niger PTCC 5010 for some industrial products, Moser and Gompertz kinetic models are able to demonstrate cell growth behavior and its substrate uptake profile. In continuous processes, dilution rate could be determined based on obtained maximum specific cell growth rate equal to 0.024 h-1.

Item Type: Article
Subjects: A General Works > AI Indexes (General)
STM Archives > Biological Science
Depositing User: Unnamed user with email support@stmarchives.com
Date Deposited: 12 Jul 2023 12:47
Last Modified: 02 Oct 2024 07:09
URI: http://science.scholarsacademic.com/id/eprint/1198

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