The pursuit of unique flavors in craft beer has led to the exploration of non-Saccharomyces (NS) yeasts. While Saccharomyces species dominate beer fermentation, NS yeasts offer flavor diversification. However, their lower fermentation efficiency and ethanol sensitivity limit their use. This study evaluated 50 NS yeast strains from Argentina’s Cuyo wine region. Torulaspora delbrueckii was selected for sequential fermentations with Saccharomyces cerevisiae due to its promising fermentative and physiological characteristics. Sequential inoculation resulted in a significant increase in fruity and spicy aromas, particularly esters like isoamyl acetate, ethyl hexanoate, and ethyl octanoate, as well as terpenes like limonene and linalool. Sensory analysis revealed that beers produced with T. delbrueckii were characterized by a more complex aroma profile, with significant increases in fruity, floral, and herbaceous notes. Additionally, the sequential fermentation strategy resulted in a higher apparent attenuation compared to pure T. delbrueckii fermentation, indicating improved sugar utilization. These findings highlight the potential of NS yeasts to enhance beer sensory characteristics. Combining NS yeasts with traditional Saccharomyces strains creates beers with distinctive flavors, expanding brewing possibilities. Sequential inoculation strategies offer a viable approach to harnessing the benefits of NS yeasts while ensuring fermentation. This research demonstrates the potential of NS yeasts to enrich the sensory experience of drinking craft beer, paving the way for further innovation in the brewing industry.

Global concern about pathogenic resistance to antibiotics is prompting interest in probiotics as a strategy to prevent or inhibit infections. Fermented beverages are promising sources of probiotic yeasts. This study aimed to evaluate the antagonistic effects of Kluyveromyces marxianus, Wickerhamomyces anomalus, and Pichia manshurica strains from kefir and wine against Salmonella enterica serovar Enteritidis in intestinal epithelial cells. The ability of these yeasts to adhere to Caco-2/TC-7 cells was evaluated, as well as their influence on the ability of Salmonella to associate and invade these cells. The behavior of the pathogen was analyzed by (a) incubation of enterocytes with yeast before adding Salmonella, (b) co-incubation of Salmonella with yeast before contact with the enterocytes, and (c) incubation of Salmonella with yeast metabolites before contact with enterocytes. All yeast strains demonstrated adherence to Caco-2/TC-7 cells (33–100%) and effectively inhibited Salmonella invasion. Among the treatments, co-culture showed the greatest effect, reducing Salmonella association and invasion by more than 50%. Additionally, these yeasts modulated the epithelial immune response, significantly decreasing CCL20-driven luminescence by 60–81% (p < 0.0001). These results highlight the potential of yeasts from fermented beverages as probiotics to counteract Salmonella infections, offering a promising alternative in the fight against antibiotic resistance.

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.