An artificial neural network as a tool for kombucha fermentation improvement

Abstract

Kombucha as a tea-based fermented beverage has become progressively widespread, mainly in the functional food market, because of health-improving benefits. As part of a daily diet for adults and children, kombucha was a valuable non-alcoholic drink containing beneficial mixtures of organic acids, minerals, vitamins, proteins, polyphenols, etc. The influence of the specific surface area of the vessel, the inoculum size, and the initial tea concentration as operating factors and fermentation time as output variable on the efficiency of kombucha fermentation was examined. The focus of this study is optimization and standardization of kombucha fermentation conditions using Box-Behnken experimental design and applying an artificial neural network (ANN) predictive model for the fermentation process. The Broyden-Fletcher-Goldfarb-Shanno iterative algorithm was used to accelerate the calculation. The obtained ANN models for the pH value and titratable acidity showed good prediction capabilities (the r2 values during the training cycle for output variables were 0.990 and 0.994, respectively). Predictive ANN modeling has been proven effective and reliable in establishing the optimum kombucha fermentation process using the selected operating factors.

Keywords

• experimental design
• fermentation improvement
• kombucha production
• mathematical modelling