期刊
ENGINEERING IN LIFE SCIENCES
卷 17, 期 8, 页码 874-880出版社
WILEY
DOI: 10.1002/elsc.201700044
关键词
Bioprocess monitoring; Fluorescence spectroscopy; Neural network; Saccharomyces cerevisiae
资金
- German Research Foundation DFG [HI 475/8-1]
The feasibility of using a feed-forward neural network in combination with 2D fluorescence spectroscopy to monitor the state of Saccharomyces cerevisiae fermentation was investigated. The main point is that for the backpropagation training of the neural network, no offlinemeasurement valuewas used, which is the ordinary approach. Instead, a theoretical model of the process has been applied to simulate the process state (biomass, glucose, and ethanol concentration) at any given time. However, the kinetic parameters of the simulation model are unknown at the beginning of the training. Itwill be demonstrated that the kinetic parameters of the theoretical process model as well as the parameters of the feed-forward neural network to predict the process state from 2D fluorescence spectra can be acquired fromthe 2D fluorescence spectra alone. Offlinemeasurements are not actually required. The resulting trained neural network can predict the process state as accurate as a conventionally ( with offline measurements) trained neural network. The calculated parameters result in a simulation model that is at least as accurate as a model with parameters acquired by least squares fitting to the offline measurements.
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