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Principal component analysis for minimal model identification of a noise-affected fermentation: Application to streptokinase

DIR@IMTECH: CSIR-Institute of Microbial Technology

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Title Principal component analysis for minimal model identification of a noise-affected fermentation: Application to streptokinase
 
Creator Patnaik, P R
 
Subject QR Microbiology
 
Description Principal component analysis (PCA) has been applied to a fed-batch fermentation for the production of streptokinase
to identify the variables which are essential to formulate an adequate model. To mimic an industrial situation,
Gaussian noise was introduced in the feed rate of the substrate. Both in the presence and in the absence of noise,
the same five variables out of seven were selected by PCA. The minimal model trained separately without and with
noise was able to predict satisfactorily the course of the fermentation for a condition not employed in training.
These observations attest the suitability of PCA to formulate minimal models for industrial scale fermentations.
 
Publisher Springer Science
 
Date 2000
 
Type Article
PeerReviewed
 
Format application/pdf
 
Identifier http://crdd.osdd.net/open/852/1/patnaik2000.2.pdf
Patnaik, P R (2000) Principal component analysis for minimal model identification of a noise-affected fermentation: Application to streptokinase. Biotechnology Letters, 22 (5). pp. 393-397. ISSN 01415492
 
Relation http://dx.doi.org/10.1023/A:1005628819371
http://crdd.osdd.net/open/852/