Neural networks as 'software sensors' in enzyme production.
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 reactions and to the lack of suitable sensors. 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+2 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. On-line state estimation and multi-step ahead prediction of enzyme activity and biomass concentration, both in a yeast lipase and fungal glucoamylase production could be satisfactorily carried out. Results showed an excellent fit for estimated lipase activity (R2 = 0.988) and biomass concentration (R2 = 0.989). In glucoamylase production, both enzyme activity and biomass concentration could also be reliably predicted for 2 time intervals (10 h) ahead with only on-line measurable parameter values as the input data.
Linko
S
S
Laboratory of Bioprocess Engineering, Helsinki University of Technology, Espoo, Finland. susan.linko@hut.fi
Luopa
J
J
Zhu
Y H
YH
D000465:Algorithms; D015374:Biosensing Techniques; D001709:Biotechnology / Q000379:methods*; D004798:Enzymes / Q000378:metabolism*; D005087:Glucan 1,4-alpha-Glucosidase / Q000378:metabolism; D008049:Lipase / Q000378:metabolism; D015233:Models, Statistical; D016571:Neural Networks, Computer
D004798:Enzymes; D008049:Lipase; D005087:Glucan 1,4-alpha-Glucosidase