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.

Contribution of biomass combustion to air pollutant emissions

In Portugal, it was estimated that around 1.95 Mton/year of wood is used in residential wood burning for heating and cooking. Additionally, in the last decades, burnt forest area has also been increasing. These combustions result in high levels of toxic air pollutants and a large perturbation of atmospheric chemistry, interfere with climate and have adverse effects on health. Accurate quantification of the amounts of trace gases and particulate matter emitted from residential wood burning, agriculture and garden waste burning and forest fires on a regional and global basis is essential for various purposes, including: the investigation of several atmospheric processes, the reporting of greenhouse gas emissions, and quantification of the air pollution sources that affect human health at regional scales. In Southern Europe, data on detailed emission factors from biomass burning are rather inexistent. Emission inventories and source apportionment, photochemical and climate change models use default values obtained for US and Northern Europe biofuels. Thus, it is desirable to use more specific locally available data. The objective of this study is to characterise and quantify the contribution of biomass combustion sources to atmospheric trace gases and aerosol concentrations more representative of the national reality. Laboratory (residential wood combustion) and field (agriculture/garden waste burning and experimental wildland fires) sampling experiments were carried out. In the laboratory, after the selection of the most representative wood species and combustion equipment in Portugal, a sampling program to determine gaseous and particulate matter emission rates was set up, including organic and inorganic aerosol composition. In the field, the smoke plumes from agriculture/garden waste and experimental wildland fires were sampled. The results of this study show that the combustion equipment and biofuel type used have an important role in the emission levels and composition. Significant differences between the use of traditional combustion equipment versus modern equipments were also observed. These differences are due to higher combustion efficiency of modern equipment, reflecting the smallest amount of particulate matter, organic carbon and carbon monoxide released. With regard to experimental wildland fires in shrub dominated areas, it was observed that the largest organic fraction in the samples studied was mainly composed by vegetation pyrolysis products. The major organic components in the smoke samples were pyrolysates of vegetation cuticles, mainly comprising steradienes and sterol derivatives, carbohydrates from the breakdown of cellulose, aliphatic lipids from vegetation waxes and methoxyphenols from the lignin thermal degradation. Despite being a banned practice in our country, agriculture/garden waste burning is actually quite common. To assess the particulate matter composition, the smoke from three different agriculture/garden residues have been sampled into 3 different size fractions (PM2.5, PM2.5-10 and PM>10). Despite distribution patterns of organic compounds in particulate matter varied among residues, the amounts of phenolics (polyphenol and guaiacyl derivatives) and organic acids were always predominant over other organic compounds in the organosoluble fraction of smoke. Among biomarkers, levoglucosan, β-sitosterol and phytol were detected in appreciable amounts in the smoke of all agriculture/garden residues. In addition, inositol may be considered as an eventual tracer for the smoke from potato haulm burning. It was shown that the prevailing ambient conditions (such as high humidity in the atmosphere) likely contributed to atmospheric processes (e.g. coagulation and hygroscopic growth), which influenced the particle size characteristics of the smoke tracers, shifting their distribution to larger diameters. An assessment of household biomass consumption was also made through a national scale survey. The information obtained with the survey combined with the databases on emission factors from the laboratory and field tests allowed us to estimate the pollutant amounts emitted in each Portuguese district. In addition to a likely contribution to the improvement of emission inventories, emission factors obtained for tracer compounds in this study can be applied in receptor models to assess the contribution of biomass burning to the levels of atmospheric aerosols and their constituents obtained in monitoring campaigns in Mediterranean Europe.

Characterization of water-soluble organic compounds in bioaerosols by liquid chromatography and fluorescence spectroscopy

In the last decades, the effects of the air pollution have been increasing, especially in the case of the human health diseases. In order to overcome this problem, scientists have been studying the components of the air. As a part of water-soluble organic compounds, amino acids are present in the atmospheric environment as components of diverse living organisms which can be responsible for spreading diseases through the air. Liquid chromatography is one technique capable of distinguish the different amino acids from each other. In this work, aiming at separating the amino acids found in the aerosols samples collected in Aveiro, the ability of four columns (Mixed-Mode WAX-1, Mixed-Mode HILIC-1, Luna HILIC and Luna C18) to separate four amino acids (aspartic acid, lysine, glycine and tryptophan) and the way the interaction of the stationary phases of the columns with the analytes is influenced by organic solvent concentration and presence/concentration of the buffer, are being assessed. In the Mixed-Mode WAX-1 column, the chromatograms of the distinct amino acids revealed the separation was not efficient, since the retention times were very similar. In the case of lysine, in the elution with 80% (V/V) MeOH, the peaks appeared during the volume void. In the Mixed-Mode HILIC-1 column, the variation of the organic solvent concentration did not affect the elution of the four studied amino acids. Considering the Luna HILIC column, the retention times of the amino acids were too close to each other to ensure a separation among each other. Lastly, the Luna C18 column revealed to be useful to separate amino acids in a gradient mode, being the variation of the mobile phase composition in the organic solvent concentration (ACN). Luna C18 was the column used to separate the amino acids in the real samples and the mobile phase had acidified water and ACN. The gradient consisted in the following program: 0 – 2 min: 5% (V/V) ACN, 2 – 8 min: 5 – 2 % (V/V) ACN, 8 – 16 min: 2% (V/V) ACN, 16 – 20 min: 2 – 20 % (V/V) ACN, 20 – 35 min: 20 – 35 % (V/V) ACN. The aerosols samples were collected by using three passive samplers placed in two different locations in Aveiro and each sampler had two filters - one faced up and the other faced down. After the sampling, the water-soluble organic compounds was extracted by dissolution in ultra-pure water, sonication bath and filtration. The resulting filtered solutions were diluted in acidified water for the chromatographic separation. The results from liquid chromatography revealed the presence of the amino acids, although it was not possible to identify each one of them individually. The chromatograms and the fluorescence spectra showed the existence of some patterns: the samples that correspond to the up filters had more intense peaks and signals, revealing that the up filters collected more organic matter.