On sample preparation methods for fermented beverage VOCs profiling by GCxGC-TOFMS
An article from Penghan Zhang, Silvia Carlin, Cesare Lotti, Fulvio Mattivi, and Urska Vrhovsek publihsed in Metabolomics, 2020
Aromas and tastes have crucial influences on the quality of fermented beverages. The determination of aromatic compounds requires global non-targeted profiling of the volatile organic compounds (VOCs) in the beverages. However, experimental VOC profiling result depends on the chosen VOC collection method.
This study aims to observe the impact of using different sample preparation techniques [dynamic headspace (DHS), vortex-assisted liquid–liquid microextraction (VALLME), multiple stir bar sorptive extraction (mSBSE), solid phase extraction (SPE), and solid phase micro-extraction (SPME)] to figure out the most suitable sample preparation protocol for profiling the VOCs from fermented beverages.
Five common sample preparation methods were studied with beer, cider, red wine, and white wine samples. After the sample preparation, collected VOCs were analyzed by two-dimensional gas chromatography coupled with time of flight mass spectrometry (GCxGC-TOFMS).
GCxGC oven parameters can be optimized with the Box–Behnken surface response model and response measure on peak dispersion. Due to the unavoidable column and detector saturation during metabolomic analysis, errors may happen during mass spectrum construction. Profiling results obtained with different sample preparation methods show considerable variance. Common findings occupy a small fraction of total annotated VOCs. For known fermentative aromas, best coverage can be reached by using SPME together with SPE for beer, and VALLME for wine and cider.
GCxGC-TOFMS is a promising tool for non-targeted profiling on VOCs from fermented beverages. However, a proper data processing protocol is lacking for metabolomic analysis. Each sample preparation method has a specific profiling spectrum on VOC profiling. The coverage of the VOC metabolome can be improved by combining complementary methods.
Full Text: https://link.springer.com/article/10.1007/s11306-020-01718-7