SCIENCE ALERT: Characterization of volatiles in beer - fermentation type and consumer likability (Food Chem)

Editor's Note

In this study, triplicates of 24 beers from three types of fermentation (top/bottom/spontaneous) were analyzed using Gas Chromatograph with Mass-Selective Detector (GC-MSD) employing solid-phase microextraction (SPME). There were no hops-derived volatiles in bottom-fermentation beers, but they were present in top and spontaneous. Top and spontaneous had more volatiles than bottom-fermentation. The authors conclude that SPME-GC-MSD is an effective method to detect volatiles in beers that contribute to acceptability.

September 30, 2019

Food Chemistry

DOI: https://doi.org/10.1016/j.foodchem.2019.04.114

Chemical characterization of aromas in beer and their effect on consumers liking

Highlights

No hops-derived volatiles were found in bottom fermented beer samples.
Top and bottom fermented beers presented more volatiles than spontaneous.
4-Ethyguaiacol and trans-β-ionone were positive towards overall liking of beers.
Styrene had a negative and significant effect on consumer liking of beers.
A highly accurate machine learning model to predict acceptability was obtained.

Abstract

Identification of volatiles in beer is important for consumers acceptability. In this study, triplicates of 24 beers from three types of fermentation (top/bottom/spontaneous) were analyzed using Gas Chromatograph with Mass-Selective Detector (GC-MSD) employing solid-phase microextraction (SPME). Principal components analysis was conducted for each type of fermentation. Multiple regression analysis, and an artificial neutral network model (ANN) were developed with the peak-areas of 10 volatiles to evaluate/predict aroma, flavor and overall liking. There were no hops-derived volatiles in bottom-fermentation beers, but they were present in top and spontaneous. Top and spontaneous had more volatiles than bottom-fermentation. 4-Ethyguaiacol and trans-β-ionone were positive towards aroma, flavor and overall liking. Styrene had a negative effect on aroma, flavor and overall liking. An ANN model with high accuracy (R = 0.98) was obtained to predict aroma, flavor and overall liking. The use of SPME-GC-MSD is an effective method to detect volatiles in beers that contribute to acceptability.