creators_name: Patnaik, P R
type: article
datestamp: 2012-02-02 16:30:43
lastmod: 2015-01-09 10:28:10
metadata_visibility: show
title: Principal component analysis for minimal model identification of a noise-affected fermentation: Application to streptokinase
ispublished: pub
subjects: QR
full_text_status: restricted
keywords: component analysis, modelling, noise, streptokinase
note: Copyright of this article belongs to Springer Science.
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.
date: 2000
date_type: published
publication: Biotechnology Letters
volume: 22
number: 5
publisher: Springer Science
pagerange: 393-397
id_number: doi:10.1023/A:1005628819371
refereed: TRUE
issn: 01415492
official_url: http://dx.doi.org/10.1023/A:1005628819371
related_url_url: http://www.springerlink.com/content/u550g4576858tx25/
related_url_type: pub
citation:   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     
document_url: http://crdd.osdd.net/open/852/1/patnaik2000.2.pdf