Multi-objective optimization of germination process parameters of browntop millet (Brachiaria ramosa) grains for improved bioactive compound production using response surface methodology and genetic algorithm approaches

Browntop millet (Urochloa ramosa syn. Brachiaria ramosa) is a nutrient-rich minor millet known for its resilience to harsh environments and potential as a functional food ingredient. Germination is a bioprocess that can enhance its nutritional value by increasing bioactive compounds and reducing antinutritional factors. The present study investigated the effects of germination conditions on the accumulation of bioactive compounds and the reduction of phytic acid and tannins in browntop millet grains. A Box–Behnken design was employed with three independent variables—soaking time (8–16 h), germination temperature (25–45 °C), and germination time (24–72 h). Response surface methodology (RSM) and a genetic algorithm (GA) were applied to model and optimize responses, including total phenolic content, total flavonoid content, antioxidant activity, ascorbic acid, γ-aminobutyric acid (GABA), and antinutritional factors. Statistical analysis indicated that the RSM empirical model predicted experimental results with high accuracy. Optimization based on the desirability function identified 12 h soaking, 33 °C germination temperature, and 48 h germination time as optimal conditions. Under these conditions, the maximum levels obtained were: total phenolic content 16.30 mg GAE/100 g, total flavonoid content 2.63 mg QUE/100 g, antioxidant activity 81.33%, ascorbic acid 4.00 mg/100 g, GABA 16.38 mg/100 g, phytic acid 0.32 mol/kg, and tannins 0.19 mg/100 g. Germination under optimized conditions significantly enhanced the nutritional and functional quality of browntop millet by increasing antioxidant and bioactive components while reducing antinutritional factors. The Box–Behnken design combined with RSM provided more accurate and efficient optimization compared to the genetic algorithm, demonstrating its suitability for modelling germination-based biofortification in functional grains.