User-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machine|
Chen, Fudi; Li, Hao; Xu, Zhihan; Hou, Shixia & Yang, Dazuo
Background: In the field of microbial fermentation technology, how to optimize the fermentation conditions is of
great crucial for practical applications. Here, we use artificial neural networks (ANNs) and support vector
machine (SVM) to offer a series of effective optimization methods for the production of iturin A. The
concentration levels of asparagine (Asn), glutamic acid (Glu) and proline (Pro) (mg/L) were set as
independent variables, while the iturin A titer (U/mL) was set as dependent variable. General regression
neural network (GRNN), multilayer feed-forward neural networks (MLFNs) and the SVM were developed.
Comparisons were made among different ANNs and the SVM.
Results: The GRNN has the lowest RMS error (457.88) and the shortest training time (1 s), with a steady
fluctuation during repeated experiments, whereas the MLFNs have comparatively higher RMS errors and
longer training times, which have a significant fluctuation with the change of nodes. In terms of the SVM, it
also has a relatively low RMS error (466.13), with a short training time (1 s).
Conclusion: According to the modeling results, the GRNN is considered as the most suitable ANN model for the
design of the fed-batch fermentation conditions for the production of iturin A because of its high robustness
and precision, and the SVM is also considered as a very suitable alternative model. Under the tolerance of 30%,
the prediction accuracies of the GRNN and SVM are both 100% respectively in repeated experiments.
Artificial neural network; Fed-batch fermentation; General regression neural network; Iturin A; Support vector machine