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

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

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Abstract

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.

Item Type:	Article
Additional Information:	Copyright of this article belongs to Springer Science.
Uncontrolled Keywords:	component analysis, modelling, noise, streptokinase
Subjects:	Q Science > QR Microbiology
Depositing User:	Dr. K.P.S.Sengar
Date Deposited:	02 Feb 2012 16:31
Last Modified:	09 Jan 2015 10:28
URI:	http://crdd.osdd.net/open/id/eprint/844

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