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. 2023 Jul 27:14:1234884.
doi: 10.3389/fmicb.2023.1234884. eCollection 2023.

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

Rocchina Pietrafesa #  1 , Gabriella Siesto #  1   2 , Maria Tufariello #  3 , Lorenzo Palombi #  4 , Antonietta Baiano  5 , Carmela Gerardi  3 , Ada Braghieri  1 , Francesco Genovese  1 , Francesco Grieco  3 , Angela Capece  1   2
Affiliations

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

Rocchina Pietrafesa et al. Front Microbiol. .

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.

Keywords: fruit beer; indigenous strains; multivariate statistical analysis; sensory analysis; volatile organic compounds.

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Conflict of interest statement

GS and AC were employed by Spinoff StarFInn S.r.l.s. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Pearson principal component analysis performed on GC-MS data for the detection of volatile compounds.
FIGURE 2
FIGURE 2
Hierarchical clustering heat map performed on the GC-MS normalized data (distance measure using Euclidean and clustering algorithm using average). Each colored cell on the map corresponds to a concentration value in the data table, with samples in columns and metabolites in rows. Molecules indicated with ∧ are detected by GC-FID.
FIGURE 3
FIGURE 3
Radar plots of visive, tactile, olfactory (A) and gustative (B) descriptors indicated by panelists.
FIGURE 4
FIGURE 4
Principal component analysis performed on sensory (olfactive-tactile and visive) descriptors: (A) sensory data correlation circle; (B) sensory data score plot.
FIGURE 5
FIGURE 5
Partial least squares correlation between volatolomic and olfactory profiles: (A) volatolomic score plot; (B) olfactory score plot; (C) volatolomic loadings plot; (D) olfactory loadings plot.
FIGURE 6
FIGURE 6
Principal component analysis performed on consumers descriptors: (A) consumers liking data correlation circle; (B) consumers liking data score plot.
See this image and copyright information in PMC

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