Amino acid profiles and quantitative structure–property relationships for malts and beers

Abstract

Experimental amino acid concentrations of blonde and black commercial beers, brewed in Argentina, as well as national malts were subjected for the first time to Quantitative Structure–Property Relationships (QSPRs). Thus, Dragon theoretical descriptors were derived for a set of optimised amino acid structures with the purpose of assessing QSPR models. We used the statistical Replacement Method for designing the best multi-parametric linear regression models, which included structural features selected from a pool containing 1497 constitutional-, topological-, geometrical-, and electronic-type molecular descriptors. In this work QSPR results were in good agreement with experimental amino acid profiles, thus demonstrating the predictive power of the designed QSPRs. QSPR-modelling was used to predict aminograms, and was also used to estimate non-available amino acid concentrations for these malts, and beers. The developed QSPR approach showed to be an useful tool for discriminating among blonde and dark beers, and malts. This is a new application of the QSPR theory to food, in particular to chemical biomarkers of malts and beers.