Characterization of polymeric substance classes in cereal-based beverages using asymmetrical flow field-flow fractionation with a multi-detection system

Cereal-based beverages contain a complex mixture of various polymeric macromolecules including polysaccharides, peptides, and polyphenols. The molar mass of polymers and their degradation products affect different technological and especially sensory parameters of beverages. Asymmetrical flow field-flow fractionation (AF4) coupled with multi-angle light scattering (MALS) and refractive index detection (dRI) or UV detection (UV) is a technique for structure and molar mass distribution analysis of macromolecules commonly used for pure compound solutions. The objective of this study was to develop a systematic approach for identifying the polymer classes in an AF4//MALS/dRI/UV fractogram of the complex matrix in beer, a yeast-fermented cereal-based beverage. Assignment of fractogram fractions to polymer substance classes was achieved by targeted precipitations, enzymatic hydrolysis, and alignments with purified polymer standards. Corresponding effects on dRI and UV signals were evaluated according to the detector's sensitivities. Using these techniques, the AF4 fractogram of beer was classified into different fractions: (1) the low molar mass fraction was assigned to proteinaceous molecules with different degrees of glycosylation, (2) the middle molar mass fraction was attributed to protein-polyphenol complexes with a coelution of non-starch polysaccharides, and (3) the high molar mass fraction was identified as a mixture of the cell wall polysaccharides (i.e., beta-glucan and arabinoxylan) with a low content of polysaccharide-protein association. In addition, dextrins derived from incomplete starch hydrolysis were identified in all fractions and over the complete molar mass range. The ability to assess the components of an AF4 fractogram is beneficial for the targeted design and evaluation of polymers in fermented cereal-based beverages and for controlling and monitoring quality parameters.