Published in

Frontiers Media, Frontiers in Microbiology, (14), 2023

DOI: 10.3389/fmicb.2023.1234884

Links

Tools

Export citation

Search in Google Scholar

A multivariate approach to explore the volatolomic and sensory profiles of craft Italian Grape Ale beers produced with novel Saccharomyces cerevisiae strains

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Green circle
Published version: archiving allowed
Data provided by SHERPA/RoMEO

Abstract

This study investigated the influence of three Saccharomyces cerevisiae strains, selected from different matrices - CHE-3 (cherry), P4 (sourdough) and TA4-10 (grape must) - on characteristics of Italian Grape Ale (IGA) beers obtained at microbrewery scale. A multidisciplinary approach, combining results from analysis of chemical, volatile and organoleptic profiles of the beers, was adopted to underline the relationships between yeast starter and the quality of final products. Detection volatile organic compounds (VOCs) by Gas-Chromatography coupled with Mass Spectrometry (GC-MS) after extraction carried out by head-space micro-extraction (HS-SPME) revealed that the beer obtained by P4 strain differed from the others for its higher concentrations of esters, alcohols, and terpenes as confirmed by PCA (principal component analysis) and Cluster heatmap. Furthermore, sensorial analysis and consumer test showed that this sample differed from others by more pronounced notes of “fruity smell and floral” and “olfactory finesse,” and it was the most appreciated beer for smell, taste, and overall quality. Conversely, CHE-3 was the sample with the lowest concentrations of the identified volatiles and, together TA4-10, showed the highest scores for smoked, yeast, malt, and hop notes. As far as we know, these are the first results on the application of indigenous S. cerevisiae strains in the production of craft IGA beers analyzed through a complex multivariate approach.