Artificial neural network – Genetic algorithm to optimize wheat germ fermentation condition: Application to the production of two anti-tumor benzoquinones

• Published in: Food chemistry Vol. 227; pp. 264 - 270
• Main Authors: Zheng, Zi-Yi, Guo, Xiao-Na, Zhu, Ke-Xue, Peng, Wei, Zhou, Hui-Ming
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.07.2017
• Subjects: Algorithms; Antineoplastic Agents - metabolism; Artificial neural network; Benzoquinones; Benzoquinones - metabolism; Fermentation; Neural Networks (Computer); Second-order interactions; Triticum - chemistry; Triticum - genetics; Triticum - metabolism; Wheat germ; Wheat germ; Artificial neural network; Fermentation; Second-order interactions; Benzoquinones
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2017.01.077

•Artificial neural network exhibited good fitting ability in modeling the fermentation process.•A significant increase of total contents of MBQ and DMBQ was achieved.•A novel method was established to analyze two-factor interactions. Methoxy-ρ-benzoquinone (MBQ) and 2, 6-dimethoxy-ρ-benzoquinone (DMBQ) are two potential anticancer compounds in fermented wheat germ. In present study, modeling and optimization of added macronutrients, microelements, vitamins for producing MBQ and DMBQ was investigated using artificial neural network (ANN) combined with genetic algorithm (GA). A configuration of 16-11-1 ANN model with Levenberg-Marquardt training algorithm was applied for modeling the complicated nonlinear interactions among 16 nutrients in fermentation process. Under the guidance of optimized scheme, the total contents of MBQ and DMBQ was improved by 117% compared with that in the control group. Further, by evaluating the relative importance of each nutrient in terms of the two benzoquinones’ yield, macronutrients and microelements were found to have a greater influence than most of vitamins. It was also observed that a number of interactions between nutrients affected the yield of MBQ and DMBQ remarkably.