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Effective induction of phytase in Pichia pastoris fed-batch culture using an ANN pattern recognition model-based on-line adaptive control strategy

Paper ID Volume ID Publish Year Pages File Format Full-Text
4783 245 2007 8 PDF Available
Title
Effective induction of phytase in Pichia pastoris fed-batch culture using an ANN pattern recognition model-based on-line adaptive control strategy
Abstract

An on-line adaptive substrate feeding control system based on DO/pH measurements and artificial neural network pattern recognition (ANNPR) model for fed-batch cultivation processes proposed previously was successfully applied for the phytase production with recombinant Pichia pastoris. The control strategy could effectively improve the fermentation performance for both cultivation and induction phases. With the standard ANNPR model-based control strategy, the cultivation time before starting the methanol induction could be shortened for about 30%. By adequately increasing the feeding rate iterative step size of the ANNPR-based control during induction phase, methanol concentration could be automatically controlled within an optimal range, leading to an approximate three-fold stable increase in phytase activity compared with those obtained by the traditional DO-Stat method and the on-line methanol electrode-based on–off control strategy. The effectiveness, universal ability, as well as the operational simplicity of the proposed control system has been further verified in the recombinant P. pastoris fed-batch culture process.

Keywords
Fed-batch culture; On-line adaptive control; Pattern recognition; Phytase production; Pichia pastoris
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Effective induction of phytase in Pichia pastoris fed-batch culture using an ANN pattern recognition model-based on-line adaptive control strategy
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Publisher
Database: Elsevier - ScienceDirect
Journal: Biochemical Engineering Journal - Volume 37, Issue 1, 15 October 2007, Pages 26–33
Authors
, , , , , , ,
Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
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Full-text PDF Download
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