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 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.

Optimization of Bacillus subtilis cell growth effecting jiean-peptide production in fed batch fermentation using central composite design

Background Optimization of nutrient feeding was developed to improve the growth of Bacillus subtilis in fed batch fermentation to increase the production of jiean-peptide (JAA). A central composite design (CCD) was used to obtain a model describing the relationship between glucose, total nitrogen, and the maximum cell dry weight in the culture broth with fed batch fermentation in a 5 L fermentor. Results The results were analyzed using response surface methodology (RSM), and the optimized values of glucose and total nitrogen concentration were 30.70 g/L and 1.68 g/L in the culture, respectively. The highest cell dry weight was improved to 77.50 g/L in fed batch fermentation, which is 280% higher than the batch fermentation concentration (20.37 g/L). This led to a 44% increase of JAA production in fed batch fermentation as compared to the production of batch fermentation. Conclusion The results of this work improve the present production of JAA and may be adopted for other objective products' production.