Optimization of the Production of ?-Galactosidase by an Autolytic Strain of Streptococcus salivarius subsp. thermophilus

Applying neural networks as software sensors for enzyme engineering

The on-line control of enzyme-production processes is difficult, owing to the uncertainties typical of biological systems and to the lack of suitable on-line sensors for key process variables. For example, intelligent methods to predict the end point of fermentation could be of great economic value. Computer-assisted control based on artificial-neural-network models offers a novel solution in such situations. Well-trained feedforward-backpropagation neural networks can be used as software sensors in enzyme-process control; their performance can be affected by a number of factors.

Neural networks as "software sensors" in enzyme engineering

Industrial applications of enzyme technology are rapidly increasing. On-line control of enzyme production processes, however, is difficult owing to the uncertainties typical of biological systems and to the lack of suitable on-line sensors for key process variables and quality attributes. We demonstrate that well-trained feedforward backpropagation neural networks with one hidden layer can be employed to overcome such problems with no need for a priori knowledge of the relationships of the process variables involved. Neural network programs were written in Microsoft Visual C++ for Windows and implemented in a personal computer. The goodness of fit of the trained neural network to the reference data was determined by the coefficient of determination, R2. Case studies of beta-galactosidase, glucoamylase, lipase, and xylanase production processes will be used as examples.