TY  - JOUR
ID  - open844
UR  - http://dx.doi.org/10.1023/A:1005628819371
IS  - 5
A1  - Patnaik, P R
N2  - 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.
VL  - 22
TI  - Principal component analysis for minimal model identification of a noise-affected fermentation: application to streptokinase 
AV  - restricted
EP  - 397
N1  - Copyright of this article belongs to Springer Science.
Y1  - 2000///
PB  - Springer Science
JF  - Biotechnology Letters
KW  - component analysis
KW  -  modelling
KW  -  noise
KW  -  streptokinase
SN  - 01415492
SP  - 393
ER  -