Traditional fermentation models often oversimplify kinetics by treating microbial populations as physiologically homogeneous. To address this, we introduce a novel framework that explicitly incorporates cellular fitness by distinguishing the metabolically active subpopulation (“productive cells”) responsible for biosynthesis. This approach integrates established growth models (First Order Plus Dead Time and Logistic) with a modified Luedeking–Piret model (MALP), which introduces a new differential equation to dynamically quantify productive cells. This modeling study relies exclusively on experimental data available in the literature; no new experimental work was conducted. Validated against four diverse fermentation systems from published datasets, the MALP model demonstrated superior predictive accuracy, achieving coefficients of determination (R² > 0.97) for metabolite kinetics. Sensitivity analysis identified time-delay and maintenance-associated parameters as dominant factors governing system behavior. The key contribution of this work is a mechanistic equation that universally captures the real-world dynamics of metabolite production, providing a more realistic and robust framework for modeling heterogeneous bioprocesses.

The use of autochthonous yeast strains from viticultural environments represents a novel approach in the brewing industry. Probiotic-fermented beers have generated growing interest as they combine traditional brewing with the increasing demand for health-oriented functional beverages. The application of mathematical modeling to fermentation kinetics becomes a crucial tool to adequately describe and subsequently improve the performance of functional beer fermentation. The Saccharomyces cerevisiae PB101 autochthonous yeast from San Juan (Argentina) was previously selected for its probiotic potential and its exceptional technological traits in beer wort production. It was subsequently used to ferment a Kölsch-style brewer’s wort in order to evaluate both its probiotic potential and its resistance to the human digestive system. The results showed a survival percentage of 73.49 ± 0.54 and 80.17 ± 3.73 in fermentations conducted in 2024 and 2025, respectively. These fermentation assays were used to explore kinetic microbial growth, ethanol production, and critical fermentation parameters. Traditional modeling approaches often fail to adequately capture the intricacies of probiotic fermentations, particularly lag phases associated with microbial adaptation and metabolite biosynthesis. To address these limitations, this study develops an innovative and simple modeling system for modeling probiotic beer fermentation by incorporating two state variables: total and dead cells. The dynamics of these two variables were modeled using either a First Order Plus Dead Time model or a logistic growth model. Furthermore, the modified Luedeking–Piret model was used to study the delay time that exists between the production of viable cells and ethanol. The proposed models demonstrate enhanced predictive accuracy and dependability, providing a solid foundation for optimizing fermentation processes and advancing the development of functional beverages with exceptional probiotic properties.

Beer is one of the most popular alcoholic beverages globally, leading to continuous efforts to enhance its production methods. Raw materials and the production process are crucial in the brewing industry, with fermentation being a vital stage that significantly impacts beer quality. The aim of this study is to optimize the beer fermentation process by maximizing the ethanol concentration while minimizing species that adversely affect the organoleptic properties of beer. A novel optimization approach has been developed to derive an optimal, smooth, and continuous temperature profile that can be directly applied in real-world processes. This method integrates Fourier series and orthogonal polynomials for control action parameterization, in combination with evolutionary algorithms for parameter optimization. A key advantage of this methodology lies in its ability to handle a reduced parameter set efficiently, resulting in temperature profiles that are continuous and differentiable. This feature eliminates the need for post-smoothing and is particularly advantageous in biotechnological applications, where abrupt changes in temperature could negatively affect the viability of microorganisms. The optimized profiles not only enhance fermentation efficiency, but also improve the ethanol yield and reduce undesirable flavor compounds, providing a substantial improvement over current industrial practices. These advancements present significant potential for improving both the quality and consistency of beer production.