User-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machine

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 produc...

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Autores principales:	Chen,Fudi, Li,Hao, Xu,Zhihan, Hou,Shixia, Yang,Dazuo
Lenguaje:	English
Publicado:	Pontificia Universidad Católica de Valparaíso 2015
Materias:	Artificial neural network Fed-batch fermentation General regression neural network Iturin A Support vector machine
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582015000400003
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spelling	oai:scielo:S0717-345820150004000032015-12-04User-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machineChen,FudiLi,HaoXu,ZhihanHou,ShixiaYang,Dazuo Artificial neural network Fed-batch fermentation General regression neural network Iturin A Support vector machine 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.info:eu-repo/semantics/openAccessPontificia Universidad Católica de ValparaísoElectronic Journal of Biotechnology v.18 n.4 20152015-07-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582015000400003en10.1016/j.ejbt.2015.05.001
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Artificial neural network Fed-batch fermentation General regression neural network Iturin A Support vector machine
spellingShingle	Artificial neural network Fed-batch fermentation General regression neural network Iturin A Support vector machine Chen,Fudi Li,Hao Xu,Zhihan Hou,Shixia Yang,Dazuo User-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machine
description	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.
author	Chen,Fudi Li,Hao Xu,Zhihan Hou,Shixia Yang,Dazuo
author_facet	Chen,Fudi Li,Hao Xu,Zhihan Hou,Shixia Yang,Dazuo
author_sort	Chen,Fudi
title	User-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machine
title_short	User-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machine
title_full	User-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machine
title_fullStr	User-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machine
title_full_unstemmed	User-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machine
title_sort	user-friendly optimization approach of fed-batch fermentation conditions for the production of iturin a using artificial neural networks and support vector machine
publisher	Pontificia Universidad Católica de Valparaíso
publishDate	2015
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582015000400003
work_keys_str_mv	AT chenfudi userfriendlyoptimizationapproachoffedbatchfermentationconditionsfortheproductionofiturinausingartificialneuralnetworksandsupportvectormachine AT lihao userfriendlyoptimizationapproachoffedbatchfermentationconditionsfortheproductionofiturinausingartificialneuralnetworksandsupportvectormachine AT xuzhihan userfriendlyoptimizationapproachoffedbatchfermentationconditionsfortheproductionofiturinausingartificialneuralnetworksandsupportvectormachine AT houshixia userfriendlyoptimizationapproachoffedbatchfermentationconditionsfortheproductionofiturinausingartificialneuralnetworksandsupportvectormachine AT yangdazuo userfriendlyoptimizationapproachoffedbatchfermentationconditionsfortheproductionofiturinausingartificialneuralnetworksandsupportvectormachine
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User-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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