The Optimisation of Bitter Gourd-Grape Beverage Fermentation Using a Consolidated Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Approach

The present study adopted a response surface methodology (RSM) approach validated by artificial neural network (ANN) models to optimise the production of a bitter gourd-grape beverage. Aset of statistically pre-designed experiments were conducted, and the RSM optimisation model fitted to the obtained data, yielding adequately fit models for the monitored control variables R2 values for alcohol (0.79), pH (0.89), and total soluble solids (TSS) (0.89). Further validation of the RSM model fit using ANN showed relatively high accuracies of 0.98, 0.88, and 0.82 for alcohol, pH, and TSS, respectively, suggesting satisfactory predictability and adequacy of the models. A clear effect of the optimised conditions, namely fermentation time at (72 h), fermentation temperature (32.50 and 45.11 degrees C), and starter culture concentration (3.00 v/v) on the total titratable acidity (TTA), was observed with an R2 value of (0.40) and RSM model fit using ANN overall accuracy of (0.56). However, higher TTA values were observed for samples fermented for 72 h at starter culture concentrations above 3 mL. The level of 35% bitter gourd juice was optimised in this study and was considered desirable because the goal was to make a low-alcohol beverage.

Automated summary

This study employed a response surface methodology (RSM) approach and artificial neural network (ANN) models to optimize the production of a bitter gourd-grape beverage. The results showed that the models had satisfactory predictability and adequacy. The optimized conditions had a clear effect on the total titratable acidity (TTA), with higher concentrations of starter culture leading to higher TTA values. The study also optimized the level of bitter gourd juice to achieve a low-alcohol beverage.