کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
200791 1424348 2015 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
User-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machine
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی مهندسی شیمی (عمومی)
پیش نمایش صفحه اول مقاله
User-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machine
چکیده انگلیسی

BackgroundIn 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.ResultsThe 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).ConclusionAccording 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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Electronic Journal of Biotechnology - Volume 18, Issue 4, July 2015, Pages 273–280
نویسندگان
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