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Influence of malt roasting on the oxidative stability of sweet wort was evaluated based on radical intensity, volatile profile, content of transition metals (Fe and Cu) and thiols. Malt roasting had a large influence on the oxidative stability of sweet wort. Light sweet worts were more stable with low radical intensity, low Fe content, and ability to retain volatile compounds when heated. At mild roasting, the Fe content in the wort increased but remained close to constant with further roasting. Dark sweet worts were less stable with high radical intensities, high Fe content, and a decreased ability to retain volatiles. Results suggested that the Maillard reaction compounds formed during the roasting of malt are prooxidants in sweet wort. A thiol-removing capacity was observed in sweet wort, and it was gradually inhibited by malt roasting. It is possibly caused by thiol oxidizing enzymes present in the fresh malt.

a An extension of chemometric theory was experimentally explored to explain the physiochemical basis of the very high efficiency of soft modelling of data from nature. Soft modelling in self-organisation was interpreted by studying the unique chemical patterns of mutants in an isogenic barley model on endosperm development. Extremely reproducible, differential Near Infrared (NIR) spectral patterns specifically overviewed the effect on cell composition of each mutant cause. Extended Canonical Variates Analysis (ECVA) classified spectra in wild type, starch and protein mutants. The spectra were interpreted by chemometric data analysis and by pattern inspection to morphological, genetic, molecular and chemical information. Deterministic chemical reactions were defined in the glucan pathway. A drastic mutation in a gene controlling the starch/ß-glucan composition changed water activity that introduced a diffusive, stochastic effect on the catalysis of all active enzymes. 'Decision making' in self-organisation is autonomous and performed by the soft modelling of the chemical deterministic and stochastic reactions in the endosperm cell as a whole. Uncertainty in the analysis of endosperm emergence was experimentally delimited as the 'indeterminacy' in local molecular path modelling 'bottom up' and the 'irreducibility' of the phenomenological NIR spectra 'top down' . The experiment confirmed Ilya Prigogine's interpretation of self-organisation by his dynamic computer model programmed with a self-modeled non-local extension of quantum mechanics (QM). The significance of selforganisation explained by Prigogine here interpreted as physiochemical soft modelling introduces a paradigm shift in macroscopic science that forwards a major argument for soft mathematical modelling and chemometrics to obtain full scientific legitimacy.

Beyond the main bulk components of cereals such as the polysaccharides and proteins, lower concentration secondary metabolites largely contribute to the nutritional value. This paper outlines a comprehensive protocol for GC-MS metabolomic profiling of phenolics and organic acids in grains, the performance of which is demonstrated through a comparison of the metabolite profiles of the main northern European cereal crops: wheat, barley, oat and rye. Phenolics and organic acids were extracted using acidic hydrolysis, trimethylsilylated using a new method based on trimethylsilyl cyanide and analyzed by GC-MS. In order to extract pure metabolite peaks, the raw chromatographic data were processed by a multi-way decomposition method, Parallel Factor Analysis 2. This approach lead to the semi-quantitative detection of a total of 247 analytes, out of which 89 were identified based on RI and EI-MS library match. The cereal metabolome included 32 phenolics, 30 organic acids, 10 fatty acids, 11 carbohydrates and 6 sterols. The metabolome of the four cereals were compared in detail, including low concentration phenolics and organic acids. Rye and oat displayed higher total concentration of phenolic acids, but ferulic, caffeic and sinapinic acids and their esters were found to be the main phenolics in all four cereals. Compared to the previously reported methods, the outlined protocol provided an efficient and high throughput analysis of the cereal metabolome and the acidic hydrolysis improved the detection of conjugated phenolics.

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Equilibrium dialysis, molecular modeling, and multivariate data analysis were used to investigate the nature of the molecular interactions between 21 vanillin-inspired phenolic derivatives, 4 bile salts, and 2 commercially available beta-glucan preparations, Glucagel and PromOat, from barley and oats. The two beta-glucan products showed very similar binding properties. It was demonstrated that the two beta-glucan products are able to absorb most phenolic derivatives at a level corresponding to the absorption of bile salts. Glucosides of the phenolic compounds showed poor or no absorption. The four phenolic derivatives that showed strongest retention in the dialysis assay shared the presence of a hydroxyl group in para-position to a CHO group. However, other compounds with the same structural feature but possessing a different set of additional functional groups showed less retention. Principal component analysis (PCA) and partial least-squares regression (PLS) calculations using a multitude of diverse descriptors related to electronic, geometrical, constitutional, hybrid, and topological features of the phenolic compounds showed a marked distinction between aglycon, glucosides, and bile salt retention. These analyses did not offer additional information with respect to the mode of interaction of the individual phenolics with the beta-glucans. When the barley beta-glucan was subjected to enzyme degradation, the ability to bind some but not all of the phenolic derivatives was lost. It is concluded that the binding must be dependent on multiple characteristics that are not captured by a single molecular descriptor.