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An artificial neural network as a tool for kombucha fermentation improvement

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 stood
 out as a valuable non-alcoholic drink containing beneficial mixtures of
 organic acids, minerals, vitamins, proteins, polyphenols, etc. The influence
 of specific surface area of vessel, inoculum size, and initial tea
 concentration as operating parameters 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's 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 modelling demonstrated to be effective and reliable in
 establishing optimum kombucha fermentation process using selected operating
 parameters.