Fermentation seed quality analysis with self-organising neural networks

  1. Lookup NU author(s)
  2. Dr Maja Ignova
  3. Professor Gary Montague
  4. Emeritus Professor Alan Ward
  5. Professor Jarka Glassey
Author(s)Ward AC; Glassey J; Montague GA; Ignova M
Publication type Article
JournalBiotechnology and Bioengineering
ISSN (print)0006-3592
ISSN (electronic)1097-0290
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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.
PublisherJohn Wiley & Sons, Inc.
NotesTimes Cited: 11
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