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).

Fermentation diagnosis by multivariate statistical analysis / Bicciato, Silvio; Bagno, A; Solda, M; Manfredini, R; DI BELLO, C.. - In: APPLIED BIOCHEMISTRY AND BIOTECHNOLOGY. - ISSN 0273-2289. - STAMPA. - 102:(2002), pp. 49-62. [10.1385/ABAB:102-103:1-6:049]

Fermentation diagnosis by multivariate statistical analysis

BICCIATO, Silvio;
2002

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).
102
49
62
Fermentation diagnosis by multivariate statistical analysis / Bicciato, Silvio; Bagno, A; Solda, M; Manfredini, R; DI BELLO, C.. - In: APPLIED BIOCHEMISTRY AND BIOTECHNOLOGY. - ISSN 0273-2289. - STAMPA. - 102:(2002), pp. 49-62. [10.1385/ABAB:102-103:1-6:049]
Bicciato, Silvio; Bagno, A; Solda, M; Manfredini, R; DI BELLO, C.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/421534
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