A Genetic Algorithms‐Based Neural Network Model to Monitor Gibberellic Acid GA3 Fermentation Process by <i>Fusarium fujikuroi</i>

A genetic algorithm-optimized neural network (ANN-GA) was developed for real-time monitoring of gibberellin (GA3) production during Fusarium fujikuroi fermentation. This model addresses the limitations of traditional off-line detection methods, such as contamination risks and delayed feedback, by integrating six critical inputs-initial glucose concentration, fermentation time, temperature, pH, dissolved oxygen, and rotational speed-to predict glucose consumption and GA3 synthesis with an accuracy of 99.41%. During the implementation phase, by dynamically controlling the temperature (28°C-32°C) and pH, the biomass accumulation rate increased by 84% within 48 h, while the GA3 accumulation rate improved by 66.7% compared to constant-temperature fermentation at 28°C. The ANN-GA framework enables dynamic adjustment of glucose supply based on real-time predictions, thereby optimizing carbon source utilization and enhancing process stability. This data-driven approach effectively overcomes the drawbacks of costly sensors and labor-intensive manual sampling, showcasing significant potential for industrial-scale fermentation optimization. With its high accuracy and adaptability, the model holds substantial application value in advancing intelligent biological process control for secondary metabolite production.