Prediction of surfactin fermentation with <i>Bacillus subtilis</i> DSM10 by response surface methodology optimized artificial neural network
Réka Czinkóczky, Jesse John Sakiyo, Edina Eszterbauer, Áron Németh
Abstract
Abstract Biosurfactants produced by Bacillus species are an emerging group of surface‐active molecules. They have excellent surface tension reducer and high emulsifier properties. Generally, the biosurfactant fermentation leads to a low product concentration. Therefore, our goal was to investigate Bacillus subtilis DSM10 production and improve the biosurfactant content in the broth by media optimization via response surface methodology. The optimal combinations of the investigated factors were determined as the following: pH = 9, glucose = 20 g/L, and NH 4 NO 3 = 2 g/L. Under the optimized conditions, the formed surfactin strain reduced surface tension in the broth by 48% (from 72 to 37 mN/m) and the isolated product by 63% (from 72 to 27 mN/m). An artificial neural network was built based on the results of response surface methodology to predict the product quality and the harvesting time of broth. Thus, finally, the model can predict the final cell and product amount, and even their time course, with around 90% reliability.