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| Zhong,Juan; Zhang,Xiaoyong; Ren,Yanli; Yang,Jie; Tan,Hong; Zhou,Jinyan. |
| 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... |
| Tipo: Journal article |
Palavras-chave: Bacillus subtilis ZK8; Iturin A; Response surface methodology. |
| Ano: 2014 |
URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582014000300005 |
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| Chen,Fudi; Li,Hao; Xu,Zhihan; Hou,Shixia; Yang,Dazuo. |
| 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... |
| Tipo: Journal article |
Palavras-chave: Artificial neural network; Fed-batch fermentation; General regression neural network; Iturin A; Support vector machine. |
| Ano: 2015 |
URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582015000400003 |
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