Fermentation diagnosis by multivariate statistical analysis |
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Authors: | Bicciato Silvio Bagno Andrea Soldà Marco Manfredini Riccardo Di Bello Carlo |
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Institution: | (1) University of Padova, via Marzolo, 9, 35131 Padova, Italy;(2) Biofin Laboratories Srl, via F. Petrarca, 16, 46047 Porto Mantovano, Italy |
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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). |
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Keywords: | Fermentation processes process identification process diagnosis multiway principal component analysis statistical process control database mining |
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