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.
Original language | English (US) |
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Pages (from-to) | 479-485 |
Number of pages | 7 |
Journal | Food chemistry |
Volume | 293 |
DOIs | |
State | Published - Sep 30 2019 |
Externally published | Yes |
Keywords
- Beer acceptability
- Beer aromas
- Fermentation
- Gas chromatography
- Volatiles
ASJC Scopus subject areas
- Analytical Chemistry
- Food Science