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Fermentation seed quality analysis with self-organising neural networks

Lookup NU author(s): Dr Maja Ignova, Professor Gary Montague, Emeritus Professor Alan Ward, Professor Jarka Glassey


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Industrial fermentation processes operate under well defined operating conditions to attempt to minimise production variability. Variability occurs for many reasons but a long held belief is that variation in the state of the seed is highly influential. In this paper a seed stage (a batch process) of an industrial antibiotic fermentation is considered and the performance of the main production fermentations is correlated with the quality of the seed using an unsupervised Kohonen self-organising feature map (SOM). It is shown that using only seed information poor performance in the final stage fermentations can be predicted. Data from industrial penicillin G fermenters is used to demonstrate the procedure. (C) 1999 John Wiley & Sons, Inc.

Publication metadata

Author(s): Ward AC; Glassey J; Montague GA; Ignova M

Publication type: Article

Publication status: Published

Journal: Biotechnology and Bioengineering

Year: 1999

Volume: 64

Issue: 1

Pages: 82-91

Print publication date: 05/07/1999

ISSN (print): 0006-3592

ISSN (electronic): 1097-0290

Publisher: John Wiley & Sons, Inc.


DOI: 10.1002/(SICI)1097-0290(19990705)64:1<82::AID-BIT9>3.0.CO;2-5

Notes: Times Cited: 11


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