Prediction and classification of different phases in a fermentation using neural networks

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Abstract

Neural Networks were developed to identify different phases in the growth cycle of a recombinant strain of Bacillus subtilis. The network performance was enhanced by adding the first and second derivatives of the cultivation sample absorbance. The new architecture was then coupled to a Jordan network with locally recurrent processing elements in order to forecast the stages of the fermentation based on data collected during the first 5 hours.

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