【来自SCI的前沿论文】———User-friendlyoptimization approach of fed-batch fermentation conditions for theproduction of iturin A using artificial neural networks and supportvector machine
作（著）者：陈福迪
导师：王斌
学术期刊或出版社名称：ElectronicJournal of Biotechnology
期刊封面：
期刊简介：ElectronicJournal ofBiotechnology期刊于1998年创办，隶属于Elsevier集团，收录包括分子生物学、生物化学、地球环境科学以及计算应用科学等多个生物技术相关学科文章，2015年影响因子为1.403，为SCI-E和EI双收录。
DOI：http://dx.doi.org/10.1016/j.ejbt.2015.05.001
论文主要内容：
Background: In thefield of microbial fermentation technology, how to optimize thefermentation conditions is of great crucial for practicalapplications. Here, we use artificial neural networks (ANNs) andsupport vector machine (SVM) to offer a series of effectiveoptimization methods for the production of iturin A. Theconcentration levels of asparagine (Asn), glutamic acid (Glu) andproline (Pro) (mg/L) were set as independent variables, while theiturin A titer (U/mL) was set as dependent variable. Generalregression neural network (GRNN), multilayer feed-forward neuralnetworks (MLFNs) and the SVM were developed. Comparisons were madeamong different ANNs and the SVM.
Results:The GRNN has the lowest RMS error (457.88) and the shortest trainingtime (1 s), with a steady fluctuation during repeated experiments.
The MLFNs havecomparatively higher RMS errors and longer training times, which havea significant fluctuation with the change of nodes.
In terms of the SVM,it also has a relatively low RMS error (466.13), with a shorttraining time (1 s).
Conclusion:According to the modeling results, the GRNN is considered as the mostsuitable ANN model for the design of the fed-batch fermentationconditions for the production of iturin A because of its highrobustness and precision, and the SVM is also considered as a verysuitable alternative model. Under the tolerance of 30%, theprediction accuracies of the GRNN and SVM are both 100% respectivelyin repeated experiments.
作者简介：陈福迪，水产与生命学院海洋生物学2014级研究生，主要研究方向为生态毒理学建模，硕士期间曾获得2015和2016年研究生国家奖学金，参与项目《基于IPv6的水产品物联网试验系统的设计与应用》，毕业论文题目为《有机磷酸酯对双齿围沙蚕幼体急性毒性预测模型的研究》。
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