The optimization of the fermentation process of wheat germ for flavonoids and two benzoquinones using EKF-ANN and NSGA-II

Abstract

The fermentation process for maximizing the flavonoid and methoxy-ρ-benzoquinone (MBQ) + 2,6-dimethoxy-ρ-benzoquinone (DMBQ) content of wheat germ was modeled and optimized. First, agitation speed, initial pH, fermentation temperature and fermentation time were used to construct a 4-8-2 ANN model with back-propagation (BP) and extended Kalman filter (EKF) learning algorithms. The regression coefficients (R²) between experimental and predicted values indicated that EKF-ANN models had better accuracy. Second, a multi-objective optimization procedure using non-dominated sorting genetic algorithm II (NSGA-II) was performed to create non-dominated optimal solutions which gave an insight on the optimum fermentation parameters for maximum flavonoid and MBQ + DMBQ content. Third, an approach based on LINMAP was utilized for finding the final compromise solution from the obtained non-dominated optimal solutions. The optimization results show a 13.34% increase in flavonoid content and a 7.92% increase in the total content of MBQ and DMBQ simultaneously, compared with the highest values in original design.