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.
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Patnaik, P. Principal component analysis for minimal model identification of a noise-affected fermentation: application to streptokinase. Biotechnology Letters 22, 393–397 (2000). https://doi.org/10.1023/A:1005628819371
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DOI: https://doi.org/10.1023/A:1005628819371