Metabonomic studies to assess beer quality and stability

The work reported in this thesis aimed at applying the methodology known as metabonomics to the detailed study of a particular type of beer and its quality control, with basis on the use of multivariate analysis (MVA) to extract meaningful information from given analytical data sets. In Chapter 1, a detailed description of beer is given considering the brewing process, main characteristics and typical composition of beer, beer stability and the commonly used analytical techniques for beer analysis. The fundamentals of the analytical methods employed here, namely nuclear magnetic resonance (NMR) spectroscopy, gas-chromatography-mass spectrometry (GC-MS) and mid-infrared (MIR) spectroscopy, together with the description of the metabonomics methodology are described shortly in Chapter 2. In Chapter 3, the application of high resolution NMR to characterize the chemical composition of a lager beer is described. The 1H NMR spectrum obtained by direct analysis of beer show a high degree of complexity, confirming the great potential of NMR spectroscopy for the detection of a wide variety of families of compounds, in a single run. Spectral assignment was carried out by 2D NMR, resulting in the identification of about 40 compounds, including alcohols, amino acids, organic acids, nucleosides and sugars. In a second part of Chapter 3, the compositional variability of beer was assessed. For that purpose, metabonomics was applied to 1H NMR data (NMR/MVA) to evaluate beer variability between beers from the same brand (lager), produced nationally but differing in brewing site and date of production. Differences between brewing sites and/or dates were observed, reflecting compositional differences related to particular processing steps, including mashing, fermentation and maturation. Chapter 4 describes the quantification of organic acids in beer by NMR, using different quantitative methods: direct integration of NMR signals (vs. internal reference or vs. an external electronic reference, ERETIC method) and by quantitative statistical methods (using the partial least squares (PLS) regression) were developed and compared. PLS1 regression models were built using different quantitative methods as reference: capillary electrophoresis with direct and indirect detection and enzymatic essays. It was found that NMR integration results generally agree with those obtained by the best performance PLS models, although some overestimation for malic and pyruvic acids and an apparent underestimation for citric acid were observed. Finally, Chapter 5 describes metabonomic studies performed to better understand the forced aging (18 days, at 45 ºC) beer process. The aging process of lager beer was followed by i) NMR, ii) GC-MS, and iii) MIR spectroscopy. MVA methods of each analytical data set revealed clear separation between different aging days for both NMR and GC-MS data, enabling the identification of compounds closely related with the aging process: 5-hydroxymethylfurfural (5-HMF), organic acids, γ-amino butyric acid (GABA), proline and the ratio linear/branched dextrins (NMR domain) and 5-HMF, furfural, diethyl succinate and phenylacetaldehyde (known aging markers) and, for the first time, 2,3-dihydro-3,5-dihydroxy-6-methyl-4(H)-pyran-4-one xii (DDMP) and maltoxazine (by GC-MS domain). For MIR/MVA, no aging trend could be measured, the results reflecting the need of further experimental optimizations. Data correlation between NMR and GC-MS data was performed by outer product analysis (OPA) and statistical heterospectroscopy (SHY) methodologies, enabling the identification of further compounds (11 compounds, 5 of each are still unassigned) highly related with the aging process. Data correlation between sensory characteristics and NMR and GC-MS was also assessed through PLS1 regression models using the sensory response as reference. The results obtained showed good relationships between analytical data response and sensory response, particularly for the aromatic region of the NMR spectra and for GC-MS data (r > 0.89). However, the prediction power of all built PLS1 regression models was relatively low, possibly reflecting the low number of samples/tasters employed, an aspect to improve in future studies.