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dc.creatorChen,Fudi
dc.creatorLi,Hao
dc.creatorXu,Zhihan
dc.creatorHou,Shixia
dc.creatorYang,Dazuo
dc.date2015-07-01
dc.date.accessioned2019-05-03T12:45:19Z
dc.date.available2019-05-03T12:45:19Z
dc.identifierhttps://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0717-34582015000400003
dc.identifier.urihttp://revistaschilenas.uchile.cl/handle/2250/85488
dc.descriptionBackground 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.
dc.formattext/html
dc.languageen
dc.publisherPontificia Universidad Católica de Valparaíso
dc.relation10.1016/j.ejbt.2015.05.001
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceElectronic Journal of Biotechnology v.18 n.4 2015
dc.subjectArtificial neural network
dc.subjectFed-batch fermentation
dc.subjectGeneral regression neural network
dc.subjectIturin A
dc.subjectSupport vector machine
dc.titleUser-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machine


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