Journal
ELECTRONIC JOURNAL OF BIOTECHNOLOGY
Volume 18, Issue 4, Pages 273-280Publisher
UNIV CATOLICA DE VALPARAISO
DOI: 10.1016/j.ejbt.2015.05.001
Keywords
Artificial neural network; Fed-batch fermentation; General regression neural network; Iturin A; Support vector machine
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Funding
- National Marine Public Welfare Research Project [201305002, 201305043]
- National Natural Science Foundation of China [30901107]
- Project of Marine Ecological Restoration Technology Research [19-3YJ09]
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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. (C) 2015 Pontificia Universidad Catolica de Valparaiso. Production and hosting by Elsevier B.V. All rights reserved.
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