Enlarging Knowledge on Lager Beer Volatile Metabolites Using Multidimensional Gas Chromatography.
Cátia MartinsTiago BrandãoAdelaide AlmeidaSílvia M RochaPublished in: Foods (Basel, Switzerland) (2020)
Foodomics, emergent field of metabolomics, has been applied to study food system processes, and it may be useful to understand sensorial food properties, among others, through foods metabolites profiling. Thus, as beer volatile components represent the major contributors for beer overall and peculiar aroma properties, this work intends to perform an in-depth profiling of lager beer volatile metabolites and to generate new data that may contribute for molecules' identification, by using multidimensional gas chromatography. A set of lager beers were used as case-study, and 329 volatile metabolites were determined, distributed over 8 chemical families: acids, alcohols, esters, monoterpenic compounds, norisoprenoids, sesquiterpenic compounds, sulfur compounds, and volatile phenols. From these, 96 compounds are reported for the first time in the lager beer volatile composition. Around half of them were common to all beers under study. Clustering analysis allowed a beer typing according to production system: macro- and microbrewer beers. Monoterpenic and sesquiterpenic compounds were the chemical families that showed wide range of chemical structures, which may contribute for the samples' peculiar aroma characteristics. In summary, as far as we know, this study presents the most in-depth lager beer volatile composition, which may be further used in several approaches, namely, in beer quality control, monitoring brewing steps, raw materials composition, among others.
Keyphrases
- gas chromatography
- mass spectrometry
- tandem mass spectrometry
- high resolution mass spectrometry
- gas chromatography mass spectrometry
- ms ms
- solid phase extraction
- healthcare
- liquid chromatography
- high resolution
- quality control
- single cell
- electronic health record
- risk assessment
- climate change
- optical coherence tomography
- simultaneous determination
- machine learning
- big data