Unstructured kinetic modeling of mixed-culture honey-wine fermentation systems involving dual substrate and product dynamics

초록

Nitrogen supplementation is commonly used to prevent sluggish or stuck fermentations and has been extensively studied in monoculture systems. However, its effects on mixed-culture fermentations, particularly in honey wine, remain poorly understood. This study aimed to quantify the impact of nitrogen supplementation on a honey wine fermentation system co-inoculated with Saccharomyces cerevisiae and Lactobacillus hilgardii. Batch fermentations were conducted at varying initial nitrogen concentrations using diammonium phosphate (DAP) as the nitrogen source. Time-series data for microbial growth, substrate consumption (sugar and yeast assimilable nitrogen), and product formation (ethanol and lactate) were collected. Unstructured kinetic models, including logistic, Monod, dual-substrate Monod, and Moser models, were used to describe biomass dynamics, while Luedeking-Piret-type models were used to capture product formation. Parameter estimation and model validation were performed using nonlinear regression with confidence interval analysis. The logistic model effectively captured S. cerevisiae growth and substrate depletion trends (R-2 > 0.90), with strong parameter identifiability. However, it underperformed for L. hilgardii, particularly under low-nitrogen conditions due to its inability to capture extended lag phases. The Monod model provided excellent fits for ethanol and lactate production kinetics (R-2 > 0.91). Although dual-substrate models showed slightly improved fits, they suffered from poor parameter convergence. Independent validation using extreme nitrogen concentrations (100 and 300 mg/L) confirmed the robustness of the logistic and Monod models. Overall, a hybrid modeling approach, logistic models for microbial growth and substrate trends, and Monod models for product formation, provides a reliable framework for simulation, control, and scale-up of mixed-culture honey wine fermentations.

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