Detection and characterization of physiological states in bioprocesses based on Hölder exponent

Today, the pace of progress in fermentation is fast and furious, particularly since the advent of genetic engineering and the recent advances in computer sciences and process control. The high cost associated with many fermentation processes makes optimization of bioreactor performance trough command control very desirable. Clearly, control of fermentation is recognized as a vital component in the operation and successful production of many industries. Today’s advances in measurement, data acquisition and handling technologies provide a wealth of new data which can be used to improve existing models. In this article we propose a method of physiological state identification based on segmentation of bioreactor sensors signals. The underlying of this method is based on the detection of signals singularities by the Maximum of Modulus of Wavelets Transform and their characterization by Hölder exponent evaluation. The physiological states identification is based on the correlation product between biochemical signals. The efficiency of the method has been tested in a fed-batch fermentation having the goal to increase the biomass production.