Modeling and Optimization of β-Cyclodextrin Production by Bacillus licheniformis using Artiï‌cial Neural Network and Genetic Algorithm

Background: The complexity of the fermentation processes is mainly due to the complex nature of the biological systems which follow the life in a non-linear manner. Joined performance of artificial neural network (ANN) and genetic algorithm (GA) in finding optimal solutions in experimentation has found to be superior compared to the statistical methods. Range of applications of β-cyclodextrin (β-CD) as an enzymatic derivative of starch is diverse, where the complex performance of cyclodextrin glucanotransferase (CGTase) as the involved enzyme is not well recognized. Objectives: The aim of the present work was to use ANN systems with different training algorithms and defined architectures joined with GA, in order to optimize β-CD production considering temperature of the reaction mixture, substrate concentration, and the inoculum’s pH as the input variables. Materials and Methods: Commercially Neural Power, version 2.5 (CPC-X Software, 2004) was used for the numerical analysis according to the specifications provided in the software. β-CD concentration was determined spectrophotometrically according to phenolphthalein discoloration technique, described in the literature. Results: Randomly obtaining the experimental data for β-CD production in a fermentation process, could get explainable order using the ANN system coupled with GA. Changes of the β-CD as the function of each of the three selected input variables, were best quantified with use of the ANN system joined with the GA. The performance of the IBP learning algorithm was highly favorable (10300 epoch’s number within 5 second, with the lowest RMSE value) while the sensitivity analysis of the results which was carried out according to the weight method, were indicative of the importance of input variables as follows: substrate concentration < temperature < inoculum’s pH. For instance, small changes in the system’s pH are associated with the large variation in the β-CD production as has been described by the suggested model. Conclusions: Production of β-CD (enzymatic derivative of starch) by B. licheniformis was satisfactorily described based on multivariate data analysis application of the ANN system and the experimental data were optimized by considering ANN plus the GA where the IBP was used as the training method and with use of three neurons as the constructed variables in the hidden layer of the test network.