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
type: PeerReviewed
format: application/pdf
identifier: http://crdd.osdd.net/open/844/1/patnaik2000.1.pdf
relation: http://dx.doi.org/10.1023/A:1005628819371
identifier:   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://crdd.osdd.net/open/844/