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Fermentation diagnosis by multivariate statistical analysis.

Abstract：

:During the course of fermentation, online measuring procedures able to estimate the performance of the current operation are highly desired. Unfortunately, the poor mechanistic understanding of most biologic systems hampers attempts at direct online evaluation of the bioprocess, which is further complicated by the lack of appropriate online sensors and the long lag time associated with offline assays. Quite often available data lack sufficient detail to be directly used, and after a cursory evaluation are stored away. However, these historic databases of process measurements may still retain some useful information. A multivariate statistical procedure has been applied for analyzing the measurement profiles acquired during the monitoring of several fed-batch fermentations for the production of erythromycin. Multivariate principal component analysis has been used to extract information from the multivariate historic database by projecting the process variables onto a low-dimensional space defined by the principal components. Thus, each fermentation is identified by a temporal profile in the principal component plane. The projections represent monitoring charts, consistent with the concept of statistical process control, which are useful for tracking the progress of each fermentation batch and identifying anomalous behaviors (process diagnosis and fault detection).

journal_name

Appl Biochem Biotechnol

journal_title

Applied biochemistry and biotechnology

authors

Bicciato S,Bagno A,Soldà M,Manfredini R,Di Bello C

doi

10.1385/abab:102-103:1-6:049

subject

Has Abstract

pub_date

2002-07-01 00:00:00

pages

49-62

issue

1-6

eissn

0273-2289

issn

1559-0291

pii

ABAB-102-103-1-6-049

journal_volume

102-103

pub_type

杂志文章

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