The best strain for wine production: A concise review

Yeast strain selection is a primary determinant of wine fermentation performance and sensory outcome. Saccharomyces cerevisiae remained the workhorse for reliable alcoholic fermentation because of its robust ethanol tolerance, predictable kinetics, and capacity to complete sugar conversion; however, non-Saccharomyces species and indigenous strains have attracted increasing interest for enhancing complexity, acidity management, and bioprotection. This review synthesized contemporary evidence on what constitutes the “best” strain for wine production, considering fermentation reliability, stress tolerance, aroma and flavour modulation, acid and glycerol production, sulfur compound formation, and interactions with must composition. We contrasted commercial S. cerevisiae starter cultures (e.g., EC1118 and other industry strains) with indigenous and non-Saccharomyces candidates (e.g., Torulaspora delbrueckii, Lachancea thermotolerans, Metschnikowia spp.), and examined mixed/sequential inoculation strategies that intentionally combine strengths of different species. Recent advances in strain development—selection, adaptive evolution, and targeted genetic modification—were reviewed for their potential to tailor strains to climate-driven high-sugar musts and reduced-SO₂ winemaking. Practical aspects of starter management (inoculation rate, nutrient supplementation, temperature control) that mediated strain performance were summarized. The evidence suggested there was no single universally “best” strain; rather, the optimal choice was conditional on grape cultivar, desired wine style, must chemistry, and processing constraints. For many modern applications, tailored strain selection (commercial or native) and use of mixed fermentations provided the most flexible route to balance fermentation reliability with improved sensory complexity. We concluded with research priorities: systematic characterization of indigenous strains, mechanistic studies linking metabolism to aroma outcomes, and development of predictive tools to match strain × must × process for targeted wine profiles.