Beer's linguistics and chemistry: an investigation opening new research perspectives

Abstract

In the last two decades, interest in food production and consumption has progressively grown, alongside the booming popularity of craft beer, fueled by micro-breweries and home brewing. Beer is a complex mixture of compounds — from carbohydrates to proteins and ethanol — shaped by the recipe, ingredients, and production process. Less obvious is that the human tongue, in synergy with the oral cavity and nose, acts as a powerful sensor array. Tasting experiences can be viewed as “analytical sessions”, where sensory signals processed by the brain determine not only if the beer is appreciated but also which tastes and flavours are perceived.

In our study, we investigated the connection between the “objective” chemical profile of beer and the “subjective” sensory descriptions from user reviews. We analysed 88 beers using near-infrared (NIR), visible, and nuclear magnetic resonance (NMR) spectroscopy, pairing them with text reviews processed through natural language processing (NLP) tools and converted into numerical data via a bag-of-words approach. Principal Component Analysis-Generalized Canonical Analysis (PCA-GCA) revealed correlations between chemical signals and topics like “hops,” “brown colour,” and “booze”. NMR data showed the strongest correlations, especially for hops-related terms, while visible spectra linked to colour descriptors. Automated topic extraction often performed comparably to manual term selection, suggesting potential for scalable studies. Despite limitations like dataset size and beer variety, this approach shows promise for aligning chemical composition with sensory perception, with applications for product development and broader food analysis.

A novel approach integrates text corpora with analytical data through chemometrics, linking language complexity to instrumental responses. Results showed strong correlations, like NMR signals with hops-related terms and visible spectra with beer colour. This previously unexplored connection opens the door to designing food products tailored to consumer preferences. The approach is broadly applicable, from food science to medical diagnosis or aligning expert opinions with factual data.