Microbial Ecology of Lactic Acid Bacteria in Craft Breweries: A Case Study

Thesis Abstract

Industrial craft brewing systems increasingly rely on spontaneous and mixed-culture fermentations where lactic acid bacteria (LAB) shape product flavor, aroma, and safety. However, the ecological dynamics, strain-level diversity, and functional roles of LAB within small-scale breweries remain poorly understood, hindering standardization, quality control, and risk mitigation. This study aims to elucidate the microbial ecology of LAB in craft breweries, linking community structure to fermentation performance and sensory outcomes. The specific objectives are to (1) characterize LAB community composition and diversity across multiple breweries and fermentation stages using culture-independent and culture-dependent approaches; (2) identify keystone LAB species and functional traits associated with acidification, ester formation, and inhibition of spoilage organisms; (3) quantify the influence of production variables (grist composition, mash pH, wort aeration, and hitch-hiker microbiota) on LAB dynamics via multivariate analyses; (4) assess the relationship between LAB community profiles and beer quality attributes through physicochemical measurements and sensory analysis; and (5) develop a conceptual model and practical recommendations for better LAB management in craft brewing. A cross-sectional, mixed-methods design will be employed. The population comprises 12 independently operated craft breweries producing ales and sour beers in a defined region. Within each brewery, 6 fermentation batches will be sampled at three key points (post-yeast pitching, mid-fermentation, and fermentation end), yielding 216 sample events. Data collection will combine culture-independent techniques—amplicon sequencing of the 16S rRNA gene (V3–V4 region) for bacterial community profiling, quantitative PCR for LAB abundance, and metagenomic prediction of functional potential (PICRUSt2)—with culture-dependent isolation of LAB strains on MRS agar for phenotypic characterization (growth kinetics, acidification rate, gas production, and proteolytic activity). Complementary physicochemical measurements will include pH, titratable acidity, apparent attenuation, ethanol concentration, and ester/sour volatile profiles via GC-MS. Sensory analysis will be conducted using a trained panel evaluating aroma, flavor, and mouthfeel attributes. Instrumental data will be integrated with production variables extracted from brewery records. Statistical analyses will comprise alpha and beta diversity metrics, PERMANOVA, and redundancy analysis (RDA) to relate LAB composition to production parameters. Regression models and partial least squares discriminant analysis (PLS-DA) will examine associations between LAB community structure and chemical/sensory outcomes. Time-series analyses will be applied to track LAB dynamics across fermentation stages within batches. Network analysis will identify co-occurrence patterns and potential interactions among LAB species and other fermentative microbes. A thematic synthesis of producer practices will be paired with quantitative results to contextualize ecological patterns. Theoretical framing will draw on niche theory and the community assembly framework, with emphasis on deterministic versus stochastic processes and the concept of ecological fitting in artisanal fermentation. Where applicable, the study will test hypotheses related to the influence of wort composition and pH on LAB selection, and the role of LAB in modulating aroma-active compounds. Expected findings include (i) a core LAB consortium across craft breweries dominated by Lactobacillus and Pediococcus species with distinct strain-level variation linked to fermentation stage and wort chemistry; (ii) evidence that certain LAB functional traits (e.g., heterofermentative metabolism, malolactic-like decarboxylation) correlate with desirable sour beer attributes and inhibitory effects on spoilage organisms; (iii) significant associations between specific production practices (pH management, mash temperature, oxygen exposure) and LAB succession patterns; and (iv) a robust model predicting beer quality outcomes from measured LAB community metrics and functional potentials. The study contributes to knowledge by integrating microbial ecology with practical brewing parameters to illuminate how LAB shape product quality in craft contexts, offering a framework for predictive quality control and strain management. The conclusions will inform best practices for LAB monitoring, starter culture development, and process optimization in artisanal breweries, with recommendations for standard operating procedures, risk assessment, and targeted future research on strain-resolved interventions.