Optimization of probiotic beverage made from Sohiong fruit using response surface methodology and artificial neural network–genetic algorithm hybrid model

Abstract

In this study, a probiotic beverage from Sohiong (Prunus nepalensis), an underutilized wild edible fruit rich in phenolics and anthocyanins, was optimized using both Response Surface Methodology (RSM) and an Artificial Neural Network–Genetic Algorithm (ANN–GA) hybrid model. Plackett–Burman screening identified temperature, strain, and inoculum size as significant variables influencing antioxidant response. Subsequent optimization using a Box–Behnken design yielded maximum DPPH scavenging activity of 71.55%, with enhanced total phenolic content (TPC) of 160.00 mg GAE per g, total anthocyanin content (TAC) of 227.85 mg C3GE per 100 mL, and total flavonoid content (TFC) of 183.96 mg QE per 100 mL. The RSM model showed good predictive capacity (R² = 0.9467; RMSE = 1.98; AAD = 3.21%), but the ANN–GA model outperformed it with higher accuracy (R² = 0.9988; RMSE = 0.63; AAD = 1.46%). Validation under ANN–GA-optimized conditions closely matched the predicted values, with only a 0.04% deviation in DPPH. These results confirm the superior predictive fidelity of ANN–GA for nonlinear fermentation systems. The optimized Sohiong probiotic beverage demonstrates significant antioxidant activity and is a promising functional beverage. This study highlights the potential of integrating traditional RSM with modern AI tools like ANN–GA in functional food formulation and bioactive compound enrichment.