+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

The artificial neural network approach based on uniform design to optimize the fed-batch fermentation condition: application to the production of iturin A



The artificial neural network approach based on uniform design to optimize the fed-batch fermentation condition: application to the production of iturin A



Microbial Cell Factories 13(1): 54



Iturin A is a potential lipopeptide antibiotic produced by Bacillus subtilis. Optimization of iturin A yield by adding various concentrations of asparagine (Asn), glutamic acid (Glu) and proline (Pro) during the fed-batch fermentation process was studied using an artificial neural network-genetic algorithm (ANN-GA) and uniform design (UD). Here, ANN-GA based on the UD data was used for the first time to analyze the fed-batch fermentation process. The ANN-GA and UD methodologies were compared based on their fitting ability, prediction and generalization capacity and sensitivity analysis. The ANN model based on the UD data performed well on minimal statistical designed experimental number and the optimum iturin A yield was 13364.5 ± 271.3 U/mL compared with a yield of 9929.0 ± 280.9 U/mL for the control (batch fermentation without adding the amino acids). The root-mean-square-error for the ANN model with the training set and test set was 4.84 and 273.58 respectively, which was more than two times better than that for the UD model (32.21 and 483.12). The correlation coefficient for the ANN model with training and test sets was 100% and 92.62%, respectively (compared with 99.86% and 78.58% for UD). The error% for ANN with the training and test sets was 0.093 and 2.19 respectively (compared with 0.26 and 4.15 for UD). The sensitivity analysis of both methods showed the comparable results. The predictive error of the optimal iturin A yield for ANN-GA and UD was 0.8% and 2.17%, respectively. The satisfactory fitting and predicting accuracy of ANN indicated that ANN worked well with the UD data. Through ANN-GA, the iturin A yield was significantly increased by 34.6%. The fitness, prediction, and generalization capacities of the ANN model were better than those of the UD model. Further, although UD could get the insight information between variables directly, ANN was also demonstrated to be efficient in the sensitivity analysis. The results of these comparisons indicated that ANN could be a better alternative way for fermentation optimization with limited number of experiments.

Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 056241532

Download citation: RISBibTeXText

PMID: 24725635

DOI: 10.1186/1475-2859-13-54


Related references

Artificial neural network - Genetic algorithm to optimize wheat germ fermentation condition: Application to the production of two anti-tumor benzoquinones. Food Chemistry 227: 264-270, 2017

User-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machine. Electronic Journal of Biotechnology 18(4): 273-280, 2015

Application of model-predictive control based on artificial neural networks to optimize the fed-batch process for riboflavin production. Journal of Biotechnology 79(1): 39-52, 2000

Optimization of L-Asparaginase production from Isolated Aspergillus niger by using Solid State Fermentation on sesame cake via application of Genetic Algorithm, and Artificial Neural Network-based design model. Journal of Biotechnology 150: 538-539, 2010

The application of artificial neural network to the process prediction of penicillin fermentation in fed-batch. Abstracts of Papers American Chemical Society 205(1-2): BIOT 71, 1993

Artificial neural network-genetic algorithm approach to optimize media constituents for enhancing lipase production by a soil microorganism. Applied Biochemistry and Biotechnology 144(3): 225-235, 2008

Artificial neural network based experimental design procedure for enhancing fermentation development. Biotechnology and Bioengineering 44(4): 397-405, 1994

Application of experimental design approach and artificial neural network (ANN) for the determination of potential micellar-enhanced ultrafiltration process. Journal of Hazardous Materials 187(1-3): 67-74, 2011

Application of neural network and genetic algorithm in optimization of fed-batch fermentation. Abstracts of Papers American Chemical Society 224(1-2): BIOT 206, 2002

Relationship between a fuzzy logic and a steepest descent approach to optimize a feedforward artificial neural network configuration. International Journal of Neural Systems 5(4): 299-312, 1994

Use of surface response methodology to optimize culture conditions for iturin A production by Bacillus subtilis in solid-state fermentation. Journal of the Chinese Institute of Chemical Engineers 39(6): 635-643, 2008

An artificial neural network approach for the estimation of the primary production of energy from municipal solid waste and its application to the Balkan countries. Waste Management 78: 955-968, 2018

Probabilistic evaluation of ground-support interaction for deep rock excavation using artificial neural network and uniform design. Tunnelling and Underground Space Technology 32(none), 2012

Design considerations in hybrid neural optimization of fed-batch fermentation for PHB production by Ralstonia eutropha. Food And Bioprocess Technology: 2, 213-225, 2010

Prediction models in the design of neural network based ECG classifiers: a neural network and genetic programming approach. Bmc Medical Informatics and Decision Making 2: 1, 2002